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California Uses Hazus Multi-Hazard (Hazus-MH) to Reassess Safety of Hospitals

CALIFORNIA – Hazus-MH is playing a central role in the vulnerability analysis of over 1,300 hospitals that were built in California before 1973. The findings of this analysis have significant cost implications for the state.

Following the 1994 Northridge California earthquake that damaged several hospitals, the state passed Senate Bill 1953 that requires all existing hospitals be seismically evaluated and retrofitted, if needed. Hospitals most likely to collapse in an earthquake, which fall under Structural Performance Category (SPC) 1, are required to be seismically retrofitted, replaced or removed from acute care service by January 1, 2008 or 2013, if granted an extension. Other hospitals that are less likely to collapse (SPC 3-5) have until 2030 to be seismically upgraded.

A significant percentage of hospitals surveyed in California are SPC-1 facilities, constructed between 1950 and 1975. The methodology used for the seismic evaluations is NEHRP Handbook for the Seismic Evaluation of Existing Buildings (FEMA 178). Since the publication of FEMA 178 in 1992, significant progress has been made in understanding the seismic performance of buildings, especially in performance based design. Hazus-MH has contributed in a major way to our ability to assess performance of buildings in earthquakes.

In November, 2007, the California Building Standards Commission approved the use of the Hazus-MH Advanced Engineering Building Module (AEBM) to re-evaluate hospitals in California. The Commission's action amends the rule for implementing SB 1953.

The use of Hazus-MH instead of the FEMA 178 methodology will offer significant cost savings for California's hospitals. As Chris Poland, former President of the Earthquake Engineering Research Institute explained, "This new method is not only more accurate in assessing a hospital's risk of failure in a 500-year earthquake, but it also saves the state billions of dollars in repairs that do not need to be completed until 2030. Many of the buildings that are safe from collapse have been inaccurately labeled as unsafe by previous rudimentary measurements."

The application of Hazus-MH for hospital risk assessment in California has paid immediate and potentially far reaching dividends. In January 2007, the California Health Care Foundation issued a RAND report entitled, Seismic Safety: Will California's Hospitals be Ready for the Next Big Quake? The report indicates that SB 1953 could have cost $110 billion and that nearly half of the hospitals needing retrofitting would not be able to meet the 2013 deadline.

The ability to adapt the AEBM methodology to the evaluation of hospitals in California provides the state with a much more accurate assessment of the seismic safety of these essential facilities and in the process is saving the state billions of dollars.

Hazus: http://www.fema.gov/HAZUS

CASE STUDY FOR MULTIPLE HAZARDS; LOS ANGELES, CALIFORNIA, USA.

For many years, Southern California was America’s “Promised Land”, an ideal fuelled by the warm sunshine, golden beaches, and the mystery, glamour and romance of the film industry. Now it seems that the state is cursed by natural disasters, and Los Angeles, with its 13 million inhabitants, has become known as ‘hazard city’. The summary diagram has explained the interrelationships between the hazards which threaten Los Angeles, but it is necessary to give some more detail about how they affect the daily life of the residents of LA.

Earthquakes ; ·        Not only does the San Andreas Fault, marking the conservative margin between the Pacific and North American plates, cross Southern California, but LA was built across a myriad of transform faults. These include the Santa Monica fault, the San Fernando fault, and the Northridge/Santa Barbara fault. ·        Although the most violent earthquakes are predicted to occur along the San Andreas fault, earth movements frequently occur along most of the lesser known faults. ·        The most recent of 11 earthquakes to affect LA since 1970 occurred in January 1994, focusing in the Northridge area. ·        It registered 6.7 on the Richter scale, lasted for 30 seconds, and was followed by aftershocks lasting several days. ·        The quake killed 60 people, injured several thousand, caused buildings and sections of freeway to collapse, ignited fires following a gas leak and explosion in the Granada Hills area, and left 500 000 homes without power and 200 000 homes without water supplies. ·        The commonly held public response is of indifference or apathy. Many understand the risks, but feel that it will not happen to them . ·        There is no political will to fund complete universal structural improvements, since the tax raise required to fund the development would be tantamount to political suicide. ·        What can be done at a local government level is to legislate for new buildings and developments. All new high rise buildings must be designed to withstand earthquakes. This is often done by the company that owns the building, so reducing public expenditure. ·        The city government has action plans in place to deal with the immediate and after effects of an earthquake. ·        They have also had to fund the structural improvements to major public structures, e.g. highways. ·        The responsibility for information and advice presentation during earthquakes has largely fallen upon the media, which was successfully used in 1994. They have drawn up action plans to minimise damages and broadcast disruption. ·        Gas, water, and electricity have put in place high technology measures in order to minimise damage to supplies, but the cost of this has been passed on to the consumer.  Tsunamis; ·        Tsunamis are large tidal waves triggered by submarine earthquakes, which can travel across whole oceans at great speeds. ·        The 1964 Alaskan earthquake caused considerable damage in several Californian regions. ·        Although LA has escaped so far, it is considered to be a tsunami hazard prone area, particularly when the numbers and locations of some of the faults are considered.  Coastal Subsidence; ·        The threat of coastal flooding has increased measurably due to crustal subsidence. ·        Although this may in part be due to tectonic processes, the main cause has been the extraction of oil, and to a lesser extent, subterranean water. ·        Parts of Long Beach have sunk by up to 10m since 1926. Although this sinking has now been checked, parts of the harbour area lie below sea level, and are protected from flooding by a large sea wall.  Landslides and Mudflows; ·        Landslides and mudflows are a result of the interactions of many hazards. ·        They can be caused by earthquakes, heavy rain, deforestation and fires, or over urbanisation. They can even be caused by water seepage from domestic supply and usage. ·        They have been increased in frequency by the effects of urbanisation, e.g. deforestation, cutting roads through steep hillsides, and artificially channelling rivers. ·        The media is probably one of the most reliable forms of evidence for the landslides each year. ·        Landslides and mudflows can destroy houses, as whole hillsides may collapse and if people are in the vicinity, the speed of the event may catch them unaware resulting in injury or even death. ·        Most people feel that they have little control over the occurrence of landslides, therefore the majority of the responsibility falls on the city government and building companies. ·        It is rare for soil stabilisation to be carried out due to its expense, so mostly this is privately funded. ·        The response of the city government has been to restrict development on steep slopes and other risky areas and have therefore carried out risk analysis of many areas. Heavy Rain; ·        Winter storms bring rain and strong winds. These are especially severe during an El Nino event. ·        Although most rivers in the LA basin are short in length, and seasonal, they can transport large volumes of water during times of flood. ·        Deforestation and brush fires on the steep surrounding hillsides and rapid urbanisation have increased surface runoff. ·        Large dams have been built to try and hold back floodwater, but even so, the flood risk remains. ·        In February 1992, during an El Nino event, eight people died and dozens of cars and caravans were swept out to sea when, following two days of torrential rain, floodwaters poured through a caravan park south of Malibu. ·        Heavy rain also triggers landslides and mudflows.  El Ni Ô o and La Ni Ô a Events;        El Nino events seem to coincide with years of above average rainfall, and La Nina events with periods of drought, though to a lesser extent. ·        In February 1998, parts of Southern California were declared a disaster area. ·        El Nino was blamed for the serious floods, mudflows, landslides, storms, and, in the mountains, heavy snowfalls.  Drought; ·        The long dry summers associated with the Mediterranean climate of LA may be ideal for tourists, but, as the population of LA continues to grow, they put tremendous pressure on the limited water supplies. ·        Much of the city’s water supplies comes via the Colorado aqueduct from the River Colorado 400 km to the East, or the Owens Valley reservoir via the California Aqueduct. ·        The pressure on the water supplies will be intensified after the decision of Arizona to retain all of its’ Colorado allocation from 1996 onwards. Previously, Southern California had been able to use the surplus from Arizona’s requirements. ·        LA has one of the highest per capita water consumption rates in the country. During drought periods, wardens have the power to impose fines for wasteful uses of water. ·        The new Eastside reservoir projects should double the surface water storage capacity of the state, which will be used by both LA and San Diego. ·        So much river is taken from the Colorado that it almost dries up before reaching the sea, in years of drought.  Brush fires; ·        Much of the Los Angeles basin is covered in xerophytic (drought resistant) chaparral, or brush vegetation. ·        By the autumn, after six months without rain, this vegetation is tinder dry. ·        The Santa Ana is a hot, dry wind that owes its high temperature to adiabatic heating as it descends from the mountains. ·        The heat and extreme low humidity of the Santa Ana cause discomfort to humans and increase the dryness of the vegetation. ·        In these conditions, a careless spark or an electrical storm can cause serious fires. ·        In September 1970 a fire, 56 km in width, swept down from the Santa Monica Mountains in to Malibu. ·        Some 72 000 hectares of brush and 295 houses were destroyed and three people died. ·        In November 1993, the homes of several film stars were among 1000 destroyed in another severe brush fire.  Fog and Smog; ·        Advection fog occurs when cool air from the cold offshore Californian current drifts inland where it meets warm air. ·        Fog can form most afternoons between May and October, as the strength of the sea breeze increases. ·        This event can cause a temperature inversion, where warm air becomes trapped under colds. ·        When pollutants from LA’s traffic, power stations, and industry are released in to the air, they cause smog, and when they return to Earth, acid rain. Pollution is moved inshore by sea breezes. ·        Some 1,130 tons of noxious gases, including nitrous oxides, ozone, sulphur dioxides, hydrocarbons and other gases, are emitted in to LA’s atmosphere each day, helped by the 8 million cars on LA’s roads. ·        The sunshine helps to produce some of the highest ozone concentrations anywhere in the USA, the effect reaching as far east as San Bernardino. ·        The Clean Air Act set up the South Coast Air Quality Management District (SCAQMD) in 1977. It attempts to reduce pollution and enforce anti-pollution regulations, e.g.;

Þ    Strict emission levels for vehicles, power plants and industry.

Þ    Businesses with over 50 employees must organise car pooling programmes.

Þ    Catalytic convertors mandatory on all cars by the year 2000.

Þ    Organisations have ‘emission credits’ with targets to reduce the amount of pollutants. Those which meet their targets may sell unused credits to less successful firms. Target levels are gradually reduced over 10 years, forcing a clean up.

·        The SCAQMD hopes to reduce the number of petrol using cars by 1 million, and encourage the development and use of overall public transport systems, as yet lacking in LA. ·        In 1999, a health maintenance organisation confirmed a correlation between smog and hospital admissions. For each 10 m g increase in airborne particulate concentration, admission jumped 7% for chronic respiratory patients, and 3.5% for cardiovascular patients. According to another recent study, reported in the Los Angeles Times, residents showed lung damage that might be expected of someone who smoked half a pack of cigarettes per day.- 

HUMAN HAZARDS:   Crime; ·        Crime and drug related violence are rarely out of the news in Los Angeles. Nevertheless, the number of recorded crimes for 1995 appears to have fallen. Murders fell by 9%, rapes by 5%, robberies by 12% and aggravated assaults by 3%. ·        The type of crime varies according to the affluence of the district. ·        There is now a network of criminal gangs operating in LA, mainly on racial background. During 1995 there was a partial truce, but it did not cover all of the city. ·        Drug related crime is high, particularly related to the gangs. However, drug arrests fell from 290,000 to 189,000 in the five years between 1990-1995. This could be a reflection of a police policy where arrests are not made unless there is greater certainty of conviction. ·        New moves to increase community oriented policing, such as in the Anaheim district, have reduced crime by 21%. Tougher policies against gang crime has had a positive effect, but funding is, as always, a problem facing the city authorities. 

Immigrant Issues; ·        Metropolitan LA has grown rapidly in the past 50 years, both in area and population, attracting many immigrants from within the USA and from abroad. It covers 5 counties, with an estimated total population of 15.7m (1998). It is the most ethnically diverse city in the USA, but is also strongly segregated. ·        Los Angeles County (9m) has a population of 2m Hispanics, 1.25m of these being men of working age, including some who arrived as illegal immigrants from Mexico and Central America. ·        Most are young, have little money, and few qualifications. They are attracted by the Californian stereotype, but the reality they face on arrival is very different. ·        Until they can obtain a “Green Card” from the Department of Immigration, they may not work in the formal sector, nor can they obtain welfare. They are forced to take low paid jobs in the informal sector. ·        Even for legal immigrants, language difficulties may make it hard to obtain a permanent job. Low educational standards, lack of qualifications and poor health and housing have been characteristic of some immigrant and Afro-American communities, e.g. the Watts district. ·        Increasingly, the growing number of Asian immigrants are helping to redevelop run down areas of the city with help from the government funded Community Development Association. ·        Japanese and Korean immigrants, mainly highly educated professionals and business persons are developing low and medium cost housing, creating jobs, and helping to provide social services for their own communities. ·        In districts where they have settled, e.g. Norwalk, neighbourhood schools have improved, there is less street violence, and house prices have risen in response to demand. Unfortunately, there are tensions between Afro-American and Korean communities, sometimes resulting in violence. 

Housing; ·        Increased immigration has led to a lack of affordable housing. Regeneration and demolition of older housing means a lack of family housing units. Many immigrants earn less than $4 per hour, and cannot afford high rents. ·        40% of Asians and 55% of Hispanics in LA County live in overcrowded conditions, paying up to 34% of their income in rent for their homes. It is estimated that 200 000 are living in garages. ·        Once they establish themselves with regular jobs, the movement out of these conditions may become possible, starting the succession of movement in to the suburbs. ·        In immigrant areas, ethnic supermarkets are becoming established. ComparIson of Watts District and Beverly Hills;  

Urban Sprawl, and Suburbanisation; ·        The built up area of Los Angeles stretches for over 115 km from east to west. ·        This urban sprawl has taken over much of the former farmland to create a number of edge cities (exurbs), e.g. Mission Viejo in Orange County. ·        There has been significant loss of agricultural land in the state that provides 10% of US agricultural income. Orange County lost up to a third of its agricultural land with the simultaneous intensification of farming and expansion of settlement. Its population grew by over 1m between 1970 and 1990. ·        People are moving out of the centre of LA in search of pleasant environs, good schools and clean, safe streets. ·        In 1998, the estimated population of Mission Viejo was 80 470, a 10.4% increase since 1990. ·        However, this brings its own problems. Commuting is time consuming, and car sharing is now obligatory in most companies. ·        In spite of this, 82% of workers from Mission Viejo drive alone to their places of employment. There is little public transport from the exurbs in to the city centre. ·        Increasingly, social workers and police are reporting the emergence of youth gangs and latchkey kids amongst well to do children when one or more parents is away for 12-14 hours a day. ·        Insufficient community recreational provision has been made for these communities, composed mainly of professional high earners. 

Medical Problems; ·        Apart from the smog-related cardiovascular problems mentioned earlier, cities like LA have several human hazards in the form of diseases. ·        As with most large cities, there is the hazard of sexually transmitted diseases (STD’s) such as HIV and other infectious diseases such as Tuberculosis (TB)and influenza (flu) due to the high density of population. ·        TB and flu are becoming major problems as TB is highly infectious and is becoming more difficult to treat as is has developed resistance to antibiotics. It is responsible for many hundreds of deaths especially in the poorer areas that have limited access to medical care. ·        HIV is also becoming more prevalent as it can remain undetected for years by which time many other people could become infected, There is no effective treatment and the death toll rises every year. ·        The response of the national government to STD’s has been to educate the young on the importance of contraception as a preventative measure, but many people still do not know how it is transmitted. ·        In the case of TB, poor living conditions, especially damp housing, makes people more susceptible to infection. The approach to the TB problem has been to vaccinate people where possible, whereas with flu only those most at risk such as the young and elderly are vaccinated. ·        The response of people is often to avoid the poorer areas. However immigrants are rarely vaccinated and therefore infection may still be able to spread in the wider community. 

REFERENCES: 'Geography - An Integrated Approach' by David Waugh. 3rd edition, published by Nelson. Case Study 15, pp. 452-457 Geofile - “Los Angeles - Urban Issues 1996”, Sheila Morris, Jan. 1997, No. 299.Http://www.pupilvision.com/uppersixth/multihazards.htm, with thanks.

Microsoft Encarta 1999 Deluxe “Los Angeles”. © Microsoft.

Multi-hazard assessment modeling via multi-criteria analysis and GIS: a case study

  • Original Article
  • Published: 07 January 2019
  • Volume 78 , article number  47 , ( 2019 )

Cite this article

  • Hariklia D. Skilodimou 1 ,
  • George D. Bathrellos 1 ,
  • Konstantinos Chousianitis 2 ,
  • Ahmed M. Youssef 3 &
  • Biswajeet Pradhan 4 , 5  

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Multi-hazard assessment modeling comprises an essential tool in any plan that aims to mitigate the impact of future natural disasters. For a particular area they can be generated by combining assessment maps for different types of natural hazards. In the present study, the analytical hierarchy process (AHP) supported by a Geographical Information System (GIS) was utilized to initially produce assessment maps on hazards from landslides, floods and earthquakes and subsequently to combine them into a single multi-hazard map. Evaluation of the reliability of the proposed model predictions was performed through uncertainty analysis of the variables that we used for producing the final model. The drainage basin of Peneus (Pinios) River (Western Peloponnesus, Greece), an area that is prone to landslides, floods and seismic events, was selected for the implementation of the aforementioned approach. Our findings revealed that the high hazard zones are mainly distributed in the western and north-eastern part of the region under investigation. The calculated multi-hazard map, which corresponds to the potential urban development suitability map of the study area, was classified into five classes, namely of very low, low, moderate, high and very high suitability. The most suitable areas for urban development are distributed mostly in the eastern part, in agreement with the low and very low hazard level for the three considered natural hazards. In addition, by performing uncertainty analysis we showed that the spatial distribution of the suitability zones does not change significantly. Ultimately, the final map was verified using the actual inventory of landslides and floods that affected the study area. In this context, we showed that 80% of the landslide occurrences and all the recorded flood events fall within the boundaries of the moderate, low and very low suitability zones. Consequently, the predictive capacity of the applied method is quite good. Finally, the spatial distribution of the urban areas and the road network were compared with the derived suitability map and the results revealed that approximately 50% of both of them are located within areas susceptible to natural hazards. The proposed approach can be useful for engineers, planners and local authorities in spatial planning and natural hazard management.

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Skilodimou, H.D., Bathrellos, G.D., Chousianitis, K. et al. Multi-hazard assessment modeling via multi-criteria analysis and GIS: a case study. Environ Earth Sci 78 , 47 (2019). https://doi.org/10.1007/s12665-018-8003-4

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A machine learning framework for multi-hazards modeling and mapping in a mountainous area

  • Saleh Yousefi 1 ,
  • Hamid Reza Pourghasemi 2 ,
  • Sayed Naeim Emami 1 ,
  • Soheila Pouyan 2 ,
  • Saeedeh Eskandari 3 &
  • John P. Tiefenbacher 4  

Scientific Reports volume  10 , Article number:  12144 ( 2020 ) Cite this article

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This study sought to produce an accurate multi-hazard risk map for a mountainous region of Iran. The study area is in southwestern Iran. The region has experienced numerous extreme natural events in recent decades. This study models the probabilities of snow avalanches, landslides, wildfires, land subsidence, and floods using machine learning models that include support vector machine (SVM), boosted regression tree (BRT), and generalized linear model (GLM). Climatic, topographic, geological, social, and morphological factors were the main input variables used. The data were obtained from several sources. The accuracies of GLM, SVM, and functional discriminant analysis (FDA) models indicate that SVM is the most accurate for predicting landslides, land subsidence, and flood hazards in the study area. GLM is the best algorithm for wildfire mapping, and FDA is the most accurate model for predicting snow avalanche risk. The values of AUC (area under curve) for all five hazards using the best models are greater than 0.8, demonstrating that the model’s predictive abilities are acceptable. A machine learning approach can prove to be very useful tool for hazard management and disaster mitigation, particularly for multi-hazard modeling. The predictive maps produce valuable baselines for risk management in the study area, providing evidence to manage future human interaction with hazards.

Introduction

Human interactions with natural extreme events, or hazards, are increasing globally 1 . Natural disasters have affected people and natural environments generating vast economic losses around the world. However, in some developed counties disasters have been decreasing since 1900 2 , 3 .

Hazard is the probability of occurrence in a specified period and within a given area of a potentially damaging of a given magnitude 4 , 5 . The definition incorporates the concepts of location (where?), time (when, or how frequently?) and magnitude (how large?). Total risk (R) means the expected number of lives lost, person injured, damage to property, or disruption of economic activity due to a particular natural phenomenon, and is therefore the product of specific risk (RS) and elements at risk (E) 6 . In addition, RS is the expected degree of loss due to a natural phenomenon.

Landscapes around the world are reflections of diverse natural processes. The probabilities of extreme natural events are typically greater in more natural areas and are, in fact, extensions of natural systems. Exposure of people to these extreme natural processes could be reduced and limited if predictive models based on new approaches and deeper knowledge of effective factors were employed 7 . Mountainous areas are commonly sites of snow avalanches 8 , 9 , landslides 4 , 10 , floods 11 , 12 , mudflows 13 , ice avalanches 14 , soil erosion 15 , 16 , 17 , rock falls 18 , and wildfires 19 , 20 , 21 , 22 , 23 , 24 .

Most studies focus on a single hazard, even when there are multiple hazardous processes affecting the same landscapes 8 , 25 , 26 , 27 , 28 , 29 , 30 . However, hazards sometimes interact with each other. Sometimes, the mitigation of one hazardous process may intensify another’s frequency, duration, distribution, or intensity 31 . Studying natural hazards separately, especially in mountainous regions, may produce miscalculations of risk (or the probability of occurrence of the specific extreme natural event) in those areas. Multi-hazard risk assessment (the collective likelihood of experiencing an extreme natural event among a set of hazards) could be useful for controlling the interactions of hazards 32 .

Snow avalanches, landslides, wildfires, land subsidence, and floods are the most important risks in many mountainous regions of the world 8 , 18 , 33 , 34 . These five hazards can impact and interrupt systems (transportation, electrical power, water provisioning systems, and others), processes (trade, travel, extraction, shipping), places (residential areas, commercial districts, industrial areas, recreational sites), and people in risk-prone areas 33 , 34 . Multi-hazard risk mapping is an important need for land use management at provincial and national scales 4 , 33 , 34 , 35 , 36 , 37 . Multi-hazard mapping is receiving increasing attention 1 , 7 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 . Multi-hazard analysis has been conducted in mountainous regions of Iceland, New Zealand, Iran, and Tajikistan. In Iceland, a general method was developed for analysis of snow avalanche, rock-fall, and debris-flow hazards 7 . Schmidt et al. 46 developed an approach for multi-risk modeling in New Zealand, creating an adaptable software prototype that allows researchers to ‘plug in” natural processes of interest. Pourghasemi et al . 45 undertook a multi-hazard risk assessment based on machine learning methods in Fars Province, Iran. They considered floods, forest fires, and landslides in their study area. In another study, multiple hazards (rockslides, ice avalanches, periglacial debris flows, and lake-outburst floods) were assessed in a mountainous region of Tajikistan to develop a comprehensive regional-scale map of hazards for the study area 47 .

Though these studies exemplify multi-hazard mapping, a comprehensive study on multi-hazard assessment by machine learning models is lacking for mountainous areas. The development of multi-hazard risk mapping approaches using new methods is critical for effective management of hazards in some regions. Iran, in fact, is a country that has an extensive array of hazards (flood, landslide, earthquake, drought, dust storm, soil erosion, snow avalanche, etc.) due to the diverse geomorphological and climatic zones 28 , 12 , 48 , 49 , 50 . Snow avalanches, landslides, subsidence, wildfires, and floods occur annually in the Zagros and Alborz mountain chains in Iran; people are more numerous in these regions than in other parts of the country 51 , 52 , 53 , 54 . In this study, five major natural events (flood, landslides, land subsidence, snow avalanches, and wildfires) in Chaharmahal and Bakhtiari Province provide the basis for a multi-hazard risk assessment map. The main objective is to evaluate machine learning models as useful, universal, and accurate multi-hazard mapping products that can be applied by land use managers and planners. Based on a review of the literature, we have selected a set of machine learning models including; generalized linear model (GLM) 55 , 56 , 57 , random forest (RF) 17 , 58 , 59 , a support vector machine (SVM) 60 , 61 , 62 , boosted regression trees (BRT) 63 , 64 , 65 , mixture discriminate analysis (MDA) 66 , 56 , multivariate adaptive regression splines (MARS) 67 , 56 , 68 , and functional discriminant analysis (FDA) 17 , 66 for multi-hazard mapping. Finally, based on the accuracies of the models, available data, and the sources of models, the SVM, GLM and FDA algorithms were used to map hazards in the study area.

Chaharmahal and Bakhtiari Province is in southwestern Iran (Fig.  1 ). It is defined by a rectangle with sides at 31° 9′ and 32° 38′ N and 49°30′ and 51° 26′ E. The province contains ten counties (Ardal, Borojen, Shahrekurd, Farsan, Lordegan, Kiar, Khanmirza, Kohhrang, Ben and Saman) and covers an area of 16,553 km 2 (1% of Iran). Elevation ranges from 778 m to 4,203 m above sea level and the mean elevation in the province is 2,153 m, making it the highest province in Iran. The population of the province is 947,000. Floods, mass movements, land subsidence, wildfires, and snow avalanches occur here regularly 69 .

figure 1

Location of the study area of Chaharmahal and Bakhtiari Province, Iran.

Methodology

This study involved three main activities (Fig.  2 ): (1) Collection of extreme event data through extensive field work and assessments of government reports over a 42-year period (1977–2019); (2) Identification of the most important effective factors for each hazard through a literature review; (3) Hazard modeling using a generalized linear model (GLM), a support vector machine (SVM) model, and a functional discriminant analysis (FDA) model and construction of multi-hazard risk maps (MHRM) using the models that were most accurate for each hazard.

figure 2

Flowchart of the study methodology.

Hazards data inventory

This study identified 3,455-point locations signifying the sites of five types of extreme hazardous events that occurred over a 42-year period in the Chaharmahal and Bakhtiari province through field surveys and examination of scientific reports (Fig.  3 ). These events included 246 snow avalanches, 97 wildfires, 346 floods, 868 landslides, and 1902 cases of land subsidence. The machine learning models in this study required data from both hazard and non-hazard locations to conduct modeling. Equal numbers of non-hazard locations were randomly sampled to balance the hazard locations 21 , 22 , 23 , 66 , 70 . The samples were divided into two groups for training (70%) and for validation (30%) 21 , 23 , 24 .

figure 3

Distribution of the occurrence of the five hazards between 1977 and 2019 in Chaharmahal and Bakhtiari Province ( a ), and images of the five natural extreme events in the study area ( b ) taken by Saleh Yousefi (First author).

Data collection of the effective factors for five hazards

Based on both a review of previous studies and a compilation of experts’ suggestions, effective factors for each hazard were measured and mapped in raster layers of 10 × 10 m pixel size in ArcGIS 10.4.2. The effective factors (Table 1 ) fell into five categories: topography (DEM, slope, topographic wetness index, plan curvature, aspect, and convergence index), geology (lithology and distance from a fault), hydrology and climatology (precipitation, distance from a river, groundwater depth, drainage density, absolute minimum temperature, wind exposure index, absolute maximum temperature, and snow depth), society (distance from a road and distance from an urban areas), and vegetation/land cover (land use and NDVI). The topographic factors were extracted from 1:25,000 topographic maps obtained from the Iranian National Cartographic Center. The geological factors were acquired from a geologic map at a scale of 1:100,000, acquired from the Iranian Geology Organization. Hydrological and climatic factors were measured using data from 28 meteorological stations, digital stream layers, and 895 piezometric wells. These data were obtained from the Regional Water Company of Chaharmahal and Bakhtiari. The social factors were extracted from road networks and residential areas mapped on 1:25,000 topographic maps. The vegetation factors were discerned from Landsat 8 OLI images from June 2018. In addition, to evaluation of the importance of the effective factors for each hazard, specific factors were selected for modeling specific hazards: 12 for wildfires, 8 for snow avalanches, 12 for landslides, 12 for land subsidence, and 12 for floods.

Application of machine learning models

Three state-of-the-art machine learning models were applied in present study to construct the hazard risk maps. Each is explained below.

Functional discriminant analysis (FDA)

FDA creates a statistical method to analyze effective factors. It can generally be said that models based on discrimination do unsupervised work so that each class is subdivided into its own subclass; each subclass is given a special value 71 , 72 . The FDA model is a special combination of regression models that implements a hidden process for each class in the modeling process, especially when conducting complex class modelin 73 , 74 . The FDA model is similar to other statistical methods, so it can perform just as well 75 . But, since the FDA model is nonparametric, it has been used in a wide range of fields 76 . The FDA model is new to analyses of data, but it has been convenient to use it as a replacement for functions. Therefore, more attention should be paid to this method 77 .

Generalized linear model (GLM)

The GLM is regression-based so it can reveal differences between variables 78 . The GLM is created from several linear models, and it constructs a best regression model that can predict multiple events 79 , 80 , 81 . Some researchers have reported that GLM is most often used for spatial modeling 55 , 82 , 83 , 84 , 85 . In general, the GLM uses multiple regression to increase accuracy and quality of the results because it can establish a very clear relationship between the dependent and independent variables 86 .

Support vector machine (SVM)

SVM uses both classification and regression, based on the concept of controlled learning. Results have shown that it generates the smallest clustering errors 87 . Since this model's approach is based on statistical learning theory, it reduces errors and identifies the optimal response 88 . SVM indicates performance estimation by answering a convex optimization problem 89 , 90 . The SVM model provides a very important advantage: it identifies and analyzes layers effectively 91 .

Multi-hazards risk mapping

Snow-avalanche hazard (SAH), landslide hazard (LH), wildfire hazard (WFH), land-subsidence hazard (LSH), and flood hazard (FH) maps were created from the effective factors with the three machine learning models (Fig.  4 ). First, susceptibility to each hazard was created according to the dependent variables (locations of landslides, floods, avalanches, etc.) and some effective factors (the independent variables) using machine learning techniques. Next, the models with the highest accuracies, determined from ROC-AUC values, were selected and used for multi-hazard mapping. These models were integrated using a Boolean algorithm based on four classes for each hazard—low, moderate, high, and very high. A review of the literature 44 , 45 indicated that susceptibility classes of low and moderate were low hazard (0) conditions and high and very high were deemed high hazard (1) conditions. To facilitate integration, the four-class maps produced for each hazard by the best models (from among the three algorithms) were reassigned these two classes: 0 and 1. The maps of the five natural events (flood, landslides, land subsidence, snow avalanches, and wildfires) were combined to create an integrated multi-hazard (MH) map (i.e., MH = SAH + LH + LSH + WFH + FH) in ArcGIS and the result was reclassified (Fig.  5 ).

figure 4

The risk maps of the five hazards created from the three machine learning models for Chahaharmahal and Bakhtiari Province.

figure 5

The multi-hazard risk map based on a combination of the five best hazard risk maps for Chaharmahal and Bakhtiari Province ( *L Landslide, LS u Land subsidence, F Flood, WF Wildfire, SA Snow avalanche).

Accuracy assessment

The accuracy of each of the MH maps was assessed with the training group data (for the goodness-of-fit test) and the validation group data (for the predictive-performance test) using area under the curve (AUC). AUC is a scalar measure that is a threshold-independent method 92 , 93 . An area of 1 represents perfect classification, while an area of 0.5 or less indicates poor classification of locations by a model 45 , 94 , 95 , 96 . In the present study, to produce multi-hazard susceptibility maps of snow avalanches, land subsidence, wildfires, landslides, and flood by GLM, FDA, and SVM models a special package was applied in the R software version R 3.5.3. The packages used were "svm" 60 , 97 , "glm" 55 , 63 , and "fda" 17 , 66 .

Accuracy assessments of the hazard maps using AUC

Assessing the accuracies of the three machine learning models (Table 2 ) demonstrated that FDA (for SAH), SVM (LSH), GLM (WH), SVM (LH), and SVM (FH) provided the most accurate models. The values of AUC these five models were all greater than 0.8, indicating strong classification success and confirmed that the models were acceptably accurate.

Integrated multi-hazard (MH) map

The results of the MH map show that the hazards do not overlap (Table 3 and Fig.  5 ). More than 1/6th (16.51%) of the province is free of all five hazards. Five sixths (83.49%) of Chaharmahal and Bakhtiari Province experiences at least one of the hazards.

Arid and semi-arid regions of the world experience extreme natural events that threaten the structures and daily functions of localities 98 . Natural hazards can cause a great deal of economic damages 99 , interruptions, injuries, and loss of life. Mountainous regions are among the most disaster-prone parts of the world because of their geological, climatological, and hydrological characteristics 100 , 101 .

An effective way to begin to manage natural disasters is to map hazards. The information generated can be very useful for effective planning and management of people and activities. Most natural hazards studies have focused on single hazards. Single-hazard approaches focus on hazards as independent phenomena, ignoring the domain of relationships between the hazards 32 and this may lead to miscalculations of risk 102 , 103 , 104 , 105 , 106 , 107 . A greater emphasis on the interactions between and combinations of hazards’ risks is needed 102 . Studies that have focused on multi-hazard approaches have concluded that there is collectively greater risk from the interactions of multiple hazards than is yielded by simply combining the results of single-hazard studies. The increasing use of GIS in natural resources management and the introduction of various algebraic, statistical, and empirical methods have enabled better assessments of natural hazards. The methods have been developed in different parts of the world based on different conditions and with different amounts of available data, but they have advanced the modeling process and have revealed the spatial distributions of the natural hazards in many study areas.

Several methods have been used to model and map different natural hazards. For example, flood risk has been assessed using support vector machine (SVM), frequency ratio (FR), multivariate statistical analysis, weight of evidence (WoE), analytic hierarchy process (AHP), and decision trees (DTs). The analytic hierarchy process (AHP) method is one of the most common ways to solve problems associated with the use of multiple variables 108 , 109 and it is often used in hazard assessments 110 . However, mapping processes are very sensitive to changes in expert’s judgments and to changes in weighting the input variables at the assessment scale and are significant disadvantages 109 . The most popular methods used in landslide risk assessments are neuro-fuzzy inference systems 111 , logistic regression models, analytic hierarchy process, statistical indices 112 , vector based methods 113 , and artificial neural networks 114 . For wildfire risk assessment, probabilistic models and maximum entropy models 115 , 116 , neural network (NN) 117 , 118 , 119 , fuzzy logic 120 , 121 , 122 , logistic regression (LR) 21 , 123 , 124 , 125 , decision tree (DT) 126 , the random forest (RF) 127 , 128 , 129 , and support vector machine (SVM) 24 , 130 have been used. Numerous methods have also been used for mapping snow avalanche risk: multi-criteria decision making approaches 131 , 132 , 133 , fuzzy–frequency ratio models 134 , 135 , 136 , numerical methods, dynamic models 137 , and remote sensing-based methods 138 , 139 . Though remote sensing can provide useful information about snow avalanches, the complex relationships between snow avalanches and geomorphometric variables are often overlooked, and most risk assessments are based on expert opinion. And prediction of land subsidence risk has used methods like artificial neural networks 140 , frequency ratio 141 , logistic regression 142 , and differential radar interferometry 143 .

Machine learning is another modeling technique that is increasingly used to understand the complex relationships between a wide range of independent variables like meteorological factors (winds, air pressure, storm surge, and floods) and a dependent variable 144 . Therefore, these algorithms can aid forecasting of multiple hazards simultaneously, where the environmental conditions vary considerably across a landscape 145 .

In this study, we assessed five hazards in a mountainous region of Iran. To comprehensively assess extreme natural events in the study area, multi-hazard mapping was conducted using three machine learning models. Evaluation of the accuracies of the SVM, GLM and FDA models showed that SVM is most accurate when predicting landslide, land subsidence, and flood risks. GLM is most accurate for wildfire risk. And FDA is most accurate for snow-avalanche risk prediciton (Table 2 ). The AUCs of the five best models were over 0.8, validating their strong performances 146 and demonstrating that they (more or less) accurately predicted the patterns of the hazards in the study area. The SVM method also produced very good results for mapping landslide, land subsidence, and flood risks. Li et al. 147 applied SVM with univariate and multivariate statistical methods to investigate land subsidence. Their results showed that SVM is more accurate than other algorithms they tested. Others have confirmed the high performance of SVM for similar purposes 24 , 131 , 148 , 149 . Studies of landslide risk have also revealed that highly accurate predictions were made with SVM 112 , 150 . The strong capacity of SVM to predict flood risk has also been demonstrated 151 , 152 , 153 . GLM has been used to predict wildfire risk 123 , 154 , 155 , 156 , 157 , 158 . GLMs have proven to acceptably predict wildfire risks in California 155 , 159 and Spain 156 , 160 .

The MH risk map was developed by combining the results produced by the SVM, GLM and FDA approaches. Results demonstrate that using the best machine learning models to predict several hazards yields useful information about their interactions. Multi-hazard relationships are very dependent upon the scale of analysis and the specific sets of hazards. Understanding the relationships and interactions between multiple hazards is an important challenge 103 . This study begins to fill this gap. The results show that all five hazards are absent from 16.5% of the study area. The rest of the study area, 83.5%, is likely to be impacted by at least one of the hazards, however. Pourghasemi et al. 54 mapped both the individual and collective risks posed by three hazards (floods, forest fires, and landslides) in a multi-hazard study using machine learning techniques. Others have conducted multi-hazard risk assessments, but separately for each risk 46 , 47 , 161 .

Conclusions

As mountainous areas are challenged with a wide array of natural hazards and sites within them are prone to exposures to multiple natural hazards, this study evaluated the spatial distribution of risk from multiple hazards in Chaharmahal and Bakhtiari Province, Iran, using three machine learning models (SVM, GLM and FDA). Identification of high-risk areas is the most important issue for most decision makers and natural resource managers. In this regard, we presented a multi-hazard risk map for five natural hazards (floods, landslides, land subsidence, snow avalanches, and forest fires) in the study area. Evaluation of the accuracies of the maps produced by the SVM, GLM, and FDA models showed that SVM is most accurate model for predicting landslide, land subsidence, and flood risks. GLM is best for wildfire risk prediction. And FDA is best for snow avalanche risk assessment in the region. The results indicate that 16.5% of the study area is not likely to experience any of the five natural hazards, but the rest of province (83.5%) is at risk from exposure to at least one of the five (or several or perhaps all): 11.41% is possesses snow avalanche risk, 11.07% wildfire risk, and 9.83% landslide risk. Each type of machine learning method achieved acceptable levels of accuracy in their predictions. Therefore, these results can be regarded with high confidence and may be used in future studies to examine the spatial distributions of risks from multiple hazards and to provide useful information for proactive management and hazard mitigation.

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Acknowledgments

This work was supported by College of Agriculture, Shiraz University (Grant No. 98GRC1M271143). Authors would like to thank from Dr. OLivier Jaquet, another anonymous reviewer, and Editorial Board Member (Susanna Falsaperla) comments.

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Yousefi, S., Pourghasemi, H.R., Emami, S.N. et al. A machine learning framework for multi-hazards modeling and mapping in a mountainous area. Sci Rep 10 , 12144 (2020). https://doi.org/10.1038/s41598-020-69233-2

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