Delineating flood risk areas in Greater Dhaka of Bangladesh using geoinformatics

This paper illustrates the development of flood hazard and risk maps in Greater Dhaka of Bangladesh using geoinformatics. Multi-temporal RADARSAT SAR and GIS data were employed to delineate flood hazard and risk areas for the 1998 historical flood. Flood-affected frequency and flood depth were estimated from multi-date SAR data and considered as hydrologic parameters for the evaluation of flood hazard. Using land-cover, gemorphic units and elevation data as thematic components, flood hazard maps were created by considering the interactive effect of flood frequency and flood water depth concurrently. Analysis revealed that a major portion of Greater Dhaka was exposed to high to very high hazard zones while a smaller portion (2.72%) was free from the potential flood hazard. Flood risk map according to administrative division showed that 75.35% of Greater Dhaka was within medium to very high risk areas of which 53.39% of areas are believed to be fully urbanized by the year 2010.     

Flood inundation mapping sensitivity to riverine spatial resolution and modelling approach

An innovative approach in the investigation of complex landscapes for hydraulic modelling applications is the use of terrestrial laser scanner (TLS) that can lead to a high-resolution digital elevation model (DEM). Another notable factor in flood modelling is the selection of the hydrodynamic model (1D, 2D and 1D/2D), especially in complex riverine topographies, that can influence the accuracy of flood inundation area and mapping. This paper uses different types of hydraulic–hydrodynamic modelling approaches and several types of river and riparian area spatial resolution for the implementation of a sensitivity analysis for floodplain mapping and flood inundation modelling process at ungauged watersheds. Four data sets have been used for the construction of the river and riparian areas: processed and unprocessed TLS data, topographic land survey data and typical digitized contours from 1:5000-scale topographic maps. Modelling approaches combinations consist of: one-dimensional hydraulic models (HEC-RAS, MIKE 11), two-dimensional hydraulic models (MIKE 21, MIKE 21 FM) and combinations of coupled hydraulic models (MIKE 11/MIKE 21) within the MIKE FLOOD platform. Historical flood records and estimated flooded area derived from an observed extreme flash-flood event have been used in the validation process using 2 × 2 contingency tables. Flood inundation maps have been generated for each modelling approach and landscape configuration at the lower part of Xerias River reach at Volos, Greece, and compared for assessing the sensitivity of input data and model structure uncertainty. Results provided from contingency table analysis indicate the sensitivity of floodplain modelling on the DEM spatial resolution and the hydraulic modelling approach.     

Assessment of flood inundation mapping of Surat city by coupled 1D/2D hydrodynamic modeling: a case application of the new HEC-RAS 5

Surat city of India, situated 100 km downstream of Ukai Dam and 19.4 km upstream from the mouth of River Tapi, has experienced the largest flood in 2006. The peak discharge of about 25,770 m³ s^?1 released from the Ukai Dam was responsible for a disaster. To assess the flood and find inundation in low-lying areas, simulation work is carried out under the 1D/2D couple hydrodynamic modeling. Two hundred ninety-nine cross sections, two hydraulic structures and five major bridges across the river are considered for 1D modeling, whereas a topographic map at 0.5 m contour interval was used to produce a 5 m grid, and SRTM (30 and 90 m) grid has been considered for Surat and the Lower Tapi Basin. The tidal level at the river mouth and the release from the Ukai Dam during 2006 flood are considered as the downstream and upstream boundaries, respectively. The model is simulated under the unsteady flow condition and validated for the year 2006. The simulated result shows that 9th August was the worst day in terms of flooding for Surat city and a maximum 75–77% area was under inundation. Out of seven zones, the west zone had the deepest flood and inundated under 4–5 m. Furthermore, inundation is simulated under the bank protection work (i.e., levees, retaining wall) constructed after the 2006 flood. The simulated results show that the major zones are safe against the inundation under 14,430 m³ s^?1 water releases from Ukai Dam except for the west zone. The study shows the 2D capability of new HEC-RAS 5 for flood inundation mapping and management studies.     

Estimation of the upper bound of seismic hazard curve by using the generalised extreme value distribution

Flash floods are the most common type of natural hazards that cause loss of life and massive damage to economic activities. During the last few decades, their impact increased due to rapid urbanization and settlement in downstream areas, which are desirable place for development. Wadi Asyuti, much like other wadis in the Eastern Desert of Egypt, is prone to flash flood problems. Analysis and interpretation of microwave remotely sensed data obtained from the Shuttle Radar Topography Mission (SRTM) and Tropical Rainfall Measuring Mission (TRMM) data using GIS techniques provided information on physical characteristics of catchments and rainfall zones. These data play a crucial role in mapping flash flood potentials and predicting hydrologic conditions in space and time. In order to delineate flash flood potentials in Wadi Asyuti basin, several morphometric parameters that tend to promote higher flood peak and runoff, including drainage characteristics, basin relief, texture, and geometry were computed, ranked, and combined using several approaches. The resulting flash flood potential maps, categorized the sub-basins into five classes, ranging from very low to very high flood potentials. In addition, integrating the spatially distributed drainage density, rainfall intensity, and slope gradient further highlighted areas of potential flooding within the Wadi Asyuti basin. Processing of recent Landsat-8 imagery acquired on March 15, 2014, validated the flood potential maps and offered an opportunity to measure the extent (200–900 m in width) of the flooding zone within the flash flood event on March 9, 2014, as well as revealed vulnerable areas of social and economic activities. These results demonstrated that excessive rainfall intensity in areas of higher topographic relief, steep slope, and drainage density are the major causes of flash floods. Furthermore, integration of remote sensing data and GIS techniques allowed mapping flood-prone areas in a fast and cost-effective to help decision makers in preventing flood hazards in the future.     

Flood inundation mapping and hazard assessment of Baitarani River basin using hydrologic and hydraulic model

Frequent flood is a concern for most of the coastal regions of India. The importance of flood maps in governing strategies for flood risk management is of prime importance. Flood inundation maps are considered dependable output generated from simulation results from hydraulic models in evaluating flood risks. In the present work, a continuous hydrologic-hydraulic model has been implemented for mapping the flood, caused by the Baitarani River of Odisha, India. A rainfall time-series data were fed into the hydrologic model and the runoff generated from the model was given as an input into the hydraulic model. The study was performed using the HEC-HMS model and the FLO-2D model to map the extent of flooding in the area. Shuttle Radar Topographic Mission (SRTM) 90 m Digital Elevation Model (DEM) data, Land use/Land cover map (LULC), soil texture data of the basin area were used to compute the topographic and hydraulic parameters. Flood inundation was simulated using the FLO-2D model and based on the flow depth, hazard zones were specified using the MAPPER tool of the hydraulic model. Bhadrak District was found to be the most hazard-prone district affected by the flood of the Baitarani River. The result of the study exhibited the hydraulic model as a utile tool for generating inundation maps. An approach for assessing the risk of flooding and proper management could help in mitigating the flood. The automated procedure for mapping and the details of the study can be used for planning flood disaster preparedness in the worst affected area.

     

Assessment of flood hazard,vulnerability and risk of mid-eastern Dhaka using DEM and 1D hydrodynamic model

Floods are regular feature in rapidly urbanizing Dhaka, the capital city of Bangladesh. It is observed that about 60% of the eastern Dhaka regularly goes under water every year in monsoon due to lack of flood protection. Experience gathered from past devastating floods shows that, besides structural approach, non-structural approach such as flood hazard map and risk map is effective tools for reducing flood damages. In this paper, assessment of flood hazard by developing a flood hazard map for mid-eastern Dhaka (37.16 km²) was carried out by 1D hydrodynamic simulation on the basis of digital elevation model (DEM) data from Shuttle Radar Topography Mission and the hydrologic field-observed data for 32 years (1972–2004). As the topography of the area has been considerably changed due to rapid land-filling by land developers which was observed in recent satellite image (DigitalGlobe image; Date of imagery: 7th March 2007), the acquired DEM data were modified to represent the current topography. The inundation simulation was conducted using hydrodynamic program HEC-RAS for flood of 100-year return period. The simulation has revealed that the maximum depth is 7.55 m at the southeastern part of that area and affected area is more than 50%. A flood hazard map was prepared according to the simulation result using the software ArcGIS. Finally, to assess the flood risk of that area, a risk map was prepared where risk was defined as the product of hazard (i.e., depth of inundation) and vulnerability (i.e., the exposure of people or assets to flood). These two maps should be helpful in raising awareness of inhabitants and in assigning priority for land development and for emergency preparedness including aid and relief operations in high-risk areas in the future.     

Areal extent of the 1988 flood in Bangladesh: How much did the satellite imagery show?

This paper presents the methods and results of visual interpretation of satellite imagery for estimating areal extent of the 1988 flood in Bangladesh. The main limitation of this method was that cloud-free images were not available for the peak flood period in the first week of September. Relatively cloud-free images on three selected dates in the last three weeks of September showed that areas of inundation on those dates ranged from 44000 to 60000 km² ± 5% (31 to 42% of Bangladesh). The Government of Bangladesh official estimates of the areal extent of flooding were tentative in nature, since these were based on a choropleth map of relative proportions (percentages) of flooding in different parts of the country. The official estimate of the maximum extent of flooding was 82000 km² (57% of Bangladesh), i.e. 15 to 26% larger than the area shown on satellite imagery. The actual extent of flooding was certainly larger than the estimates from satellite imagery but, perhaps, smaller than the official estimates, since a reduction of 15 to 26% of flood area in one to three weeks seemed unlikely. An alternative method of mapping flood-affected areas by using newspaper-interpreted data was attempted, but the method had limited values because of reporting bias.     

Riverine flood mapping and impact assessment using remote sensing technique: a case study of Chenab flood-2014 in Multan district,Punjab, Pakistan

The Multan district is mainly prone to riverine floods but has remained understudied. Chenab flood-2014 was the worst flood that this district experienced in recorded history. This study applies remote sensing (RS) techniques to estimate the extent, calculate duration, assess the major causes and resulting impacts of the flood-2014, using Landsat-8 OLI images. These images were obtained for pre-flood, during-flood and post-flood instances. Secondary data of flood causing factors were obtained for comprehensive analysis. Spatially trained and validated datasets were obtained through Google Earth platform and Global positioning system. The supervised classification with maximum likelihood algorithm was used to classify land use and land cover of the study area. The Modified Normalized Difference Water Index was utilized to detect flood inundation extent and duration, and Normalized Difference Vegetation Index was utilized to monitor vegetation coverage and changes. The analysis allowed us to assess flood causes, and calculate the extent of the flooded areas with duration and recession, as well as damages to standing crops and built-up areas. The results revealed that the flood-2014 occurred due to heavy rains in early September in upper Chenab catchment. The flood inundation continued for around two months, which heavily affected agriculture and built-up areas. The present study introduces practical use of RS techniques to provide basis for effective flood inundation mapping and impact assessment, as an application for early flood response and recovery in the world.

     

Large-scale rainfall–runoff–inundation modeling for upper Citarum River watershed,Indonesia

The Citarum River is one of the strategic rivers in West Java, Indonesia. Its total watershed area is approximately 1800 km². Almost every year, the overflow from the Citarum River causes the inundation of most of the upper Citarum River watershed. To prevent and mitigate flood damage, it is necessary to understand the flooding characteristics. The region, however, suffers from a lack of observational data. Therefore, to analyze the inundation caused by flooding in the upper Citarum River watershed, a rainfall–runoff–inundation (RRI) model was employed. It used the following multiple satellite-derived datasets as input data as well as for model verification: Global Satellite Mapping of Precipitation, Hydrological data and maps based on Shuttle elevation Derivatives at multiple scales, Global Mosaics of the standard MODIS land cover type data product, and Landsat 7 satellite images. Parameter calibration was performed using a Monte Carlo simulation. The simulation was performed for February 2010. The results of this study show that the RRI model identifies inundation areas in large-scale river watersheds more effectively when using multiple satellite-derived datasets compared with the observed inundation map obtained from JICA in 2010 and Landsat 7 images. The model results can be improved if high-quality observed rainfall data, topographic data, and river cross-sectional data are available.     

Monitoring Flood Extent and Forecasting Excess Runoff Risk with RADARSAT-1 Data

Earth observation from active microwave satellites such as RADARSAT-1 is an excellent tool to monitor and forecast floods. Two complementary approaches are described in this paper: (a) real time or near-real time monitoring of flood extent and (b) mapping of hydrological properties of drainage basins. Since it can penetrate through clouds, which usually occur during precipitation periods, and due to the fact that it can be programmed with different incidence angles, RADARSAT-1 enables frequent coverage over specific areas of interest. It has been used successfully to monitor a major flood of the Red River in Manitoba in 1997, by providing frequent coverage of the flood during its progression and decrease. Resulting data and images have been useful in planning the emergency measures and in assessing flood damage. RADARSAT has also the ability to characterize hydrological properties of watersheds. It has been used in agricultural catchments in Europe for mapping soil surface roughness, which affects runoff coefficients, concentration time and resistance to erosion processes. Used to complement optical data, RADARSAT has provided information on the status of land use and soil protective cover in drainage basins. This information can then be translated into parameters and coefficients that hydrological models can use for runoff and flood forecasting     

Integration between morphometric parameters,hydrologic model,and geo-informatics techniques for estimating WADI runoff (case study WADI HALYAH—Saudi Arabia)

In arid and semiarid areas, the only surface and groundwater recharge source is the runoff generated through flash floods. Lack of hydrological data in such areas makes runoff estimation extremely complicated. Flash floods are considered catastrophic phenomena posing a major hazardous threat to cities, villages, and their infrastructures. The objective of this study is to assess the flash flood hazard and runoff in Wadi Halyah and its sub-basins. Integration of morphometric parameters, geo-informatics, and hydrological models has been done to overcome the challenge of scarcity of data.Advanced Spaceborne Thermal Emission and Reflection (ASTER) data was used to prepare a digital elevation model (DEM) with 30-m resolution, and geographical information system (GIS) was used in the evaluation of network, geometry, texture, and relief features of the morphometric parameters. Thirty-eight morphometric parameters were estimated and have been linked together for producing nine effective parameters for evaluation of the flash flood hazard in the study basin.Flash flood hazard in Wadi Halyah and its sub-basins was identified and grouped into three classes depending on nine effective parameters directly influencing the flood prone areas. Calculated runoff volume of Wadi Halyah ranges from 26.7 × 10⁶ to 111.4 × 10⁶ m³ with an inundation area of 15 and 27 km² at return periods of 5 and 100 years, respectively. Mathematical relationships among rainfall depth, runoff volume, infiltration losses, and rainfall excess demonstrate a strong directly proportional relationships with correlation coefficient of about 0.99.     

Tsunami vulnerability assessment in urban areas using numerical model and GIS

Natural disasters can neither be predicted nor prevented. Urban areas with a high population density coupled with the construction of man-made structures are subjected to greater levels of risk to life and property in the event of natural hazards. One of the major and densely populated urban areas in the east coast of India is the city of Chennai (Madras), which was severely affected by the 2004 Tsunami, and mitigation efforts were severely dampened due to the non-availability of data on the vulnerability on the Chennai coast to tsunami hazard. Chennai is prone to coastal hazards and hence has hazard maps on its earth-quake prone areas, cyclone prone areas and flood prone areas but no information on areas vulnerable to tsunamis. Hence, mapping has to be done of the areas where the tsunami of December 2004 had directly hit and flooded the coastal areas in Chennai in order to develop tsunami vulnerability map for coastal Chennai. The objective of this study is to develop a GIS-based tsunami vulnerability map for Chennai by using a numerical model of tsunami propagation together with documented observations and field measurements of the evidence left behind by the tsunami in December 2004. World-renowned and the second-longest tourist beach in the world “Marina” present in this region witnessed maximum death toll due to its flat topography, resulting in an inundation of about 300 m landward with high flow velocity of the order of 2 m/s.     

Assessing flood hazard using flood marks and analytic hierarchy process approach: a case study for the 2013 flood event in Quang Nam,Vietnam

The production of flood hazard assessment maps is an important component of flood risk assessment. This study analyses flood hazard using flood mark data. The chosen case study is the 2013 flood event in Quang Nam, Vietnam. The impacts of this event included 17 deaths, 230 injuries, 91,739 flooded properties, 11,530 ha of submerged and damaged agricultural land, 85,080 animals killed and widespread damage to roads, canals, dykes and embankments. The flood mark data include flood depth and flood duration. Analytic hierarchy process method is used to assess the criteria and sub-criteria of the flood hazard. The weights of criteria and sub-criteria are generated based on the judgements of decision-makers using this method. This assessment is combined into a single map using weighted linear combination, integrated with GIS to produce a flood hazard map. Previous research has usually not considered flood duration in flood hazard assessment maps. This factor has a rather strong influence on the livelihood of local communities in Quang Nam, with most agricultural land within the floodplain. A more comprehensive flood hazard assessment mapping process, with the additional consideration of flood duration, can make a significant contribution to flood risk management activities in Vietnam.     

An effective visualization and comparison of online terrain draped with multi-sensor satellite images

The objective of this paper is to discuss the effectiveness of visualizing online 3D terrain draped with different satellite imageries. The topographic data of the study area were obtained from the contour maps of Universiti Putra Malaysia, Selangor, Malaysia. The high resolution satellite imageries used in this project involving QUICKBIRD (0.6 m resolution), IKONOS (1 m resolution), and SPOT5 (5 m resolution). R2V software was used for editing the contour data, whereas Arc GIS was used for overlaying the imageries over the 3D terrain data. Then the data were exported into Virtual Reality Markup Language to compare the effectiveness of different satellite imageries based on the data file size, imageries size, number of images tile, loading time during office hours (from 8 a.m. to 5 p.m.) and out of office hours (after 5 p.m.), frame rate per second, and visualization quality. The results revealed that IKONOS satellite imageries are better for an effective online 3D terrain visualization utilizing GIS data even though it has lower resolution compared to QUICKBIRD.     

GIS-based landslide susceptibility mapping with probabilistic likelihood ratio and spatial multi-criteria evaluation models (North of Tehran,Iran)

The aim of this study is to produce landslide susceptibility mapping by probabilistic likelihood ratio (PLR) and spatial multi-criteria evaluation (SMCE) models based on geographic information system (GIS) in the north of Tehran metropolitan, Iran. The landslide locations in the study area were identified by interpretation of aerial photographs, satellite images, and field surveys. In order to generate the necessary factors for the SMCE approach, remote sensing and GIS integrated techniques were applied in the study area. Conditioning factors such as slope degree, slope aspect, altitude, plan curvature, profile curvature, surface area ratio, topographic position index, topographic wetness index, stream power index, slope length, lithology, land use, normalized difference vegetation index, distance from faults, distance from rivers, distance from roads, and drainage density are used for landslide susceptibility mapping. Of 528 landslide locations, 70 % were used in landslide susceptibility mapping, and the remaining 30 % were used for validation of the maps. Using the above conditioning factors, landslide susceptibility was calculated using SMCE and PLR models, and the results were plotted in ILWIS-GIS. Finally, the two landslide susceptibility maps were validated using receiver operating characteristic curves and seed cell area index methods. The validation results showed that area under the curve for SMCE and PLR models is 76.16 and 80.98 %, respectively. The results obtained in this study also showed that the probabilistic likelihood ratio model performed slightly better than the spatial multi-criteria evaluation. These landslide susceptibility maps can be used for preliminary land use planning and hazard mitigation purpose.     

Landslide susceptibility mapping using rough sets and back-propagation neural networks in the Three Gorges, China

In the Three Gorges of China, there are frequent landslides, and the potential risk of landslides is tremendous. An efficient and accurate method of generating landslide susceptibility maps is very important to mitigate the loss of lives and properties caused by these landslides. This paper presents landslide susceptibility mapping on the Zigui-Badong of the Three Gorges, using rough sets and back-propagation neural networks (BPNNs). Landslide locations were obtained from a landslide inventory map, supported by field surveys. Twenty-two landslide-related factors were extracted from the 1:10,000-scale topographic maps, 1:50,000-scale geological maps, Landsat ETM + satellite images with a spatial resolution of 28.5 m, and HJ-A satellite images with a spatial resolution of 30 m. Twelve key environmental factors were selected as independent variables using the rough set and correlation coefficient analysis, including elevation, slope, profile curvature, catchment aspect, catchment height, distance from drainage, engineering rock group, distance from faults, slope structure, land cover, topographic wetness index, and normalized difference vegetation index. The initial, three-layered, and four-layered BPNN were trained and then used to map landslide susceptibility, respectively. To evaluate the models, the susceptibility maps were validated by comparing with the existing landslide locations according to the area under the curve. The four-layered BPNN outperforms the other two models with the best accuracy of 91.53 %. Approximately 91.37 % of landslides were classified as high and very high landslide-prone areas. The validation results show sufficient agreement between the obtained susceptibility maps and the existing landslide locations.     

Preliminary flood risk assessment: the case of Athens

Flood mapping, especially in urban areas, is a demanding task requiring substantial (and usually unavailable) data. However, with the recent introduction of the EU Floods Directive (2007/60/EC), the need for reliable, but cost effective, risk mapping at the regional scale is rising in the policy agenda. Methods are therefore required to allow for efficiently undertaking what the Directive terms “preliminary flood risk assessment,” in other words a screening of areas that could potentially be at risk of flooding and that consequently merit more detailed attention and analysis. Such methods cannot rely on modeling, as this would require more data and effort that is reasonable for this high-level, screening phase. This is especially true in urban areas, where modeling requires knowledge of the detailed urban terrain, the drainage networks, and their interactions. A GIS-based multicriteria flood risk assessment methodology was therefore developed and applied for the mapping of flood risk in urban areas. This approach quantifies the spatial distribution of flood risk and is able to deal with uncertainties in criteria values and to examine their influence on the overall flood risk assessment. It can further assess the spatially variable reliability of the resulting maps on the basis of the choice of method used to develop the maps. The approach is applied to the Greater Athens area and validated for its central and most urban part. A GIS database of economic, social, and environmental criteria contributing to flood risk was created. Three different multicriteria decision rules (Analytical Hierarchy Process, Weighted Linear Combination and Ordered Weighting Averaging) were applied, to produce the overall flood risk map of the area. To implement this methodology, the IDRISI Andes GIS software was customized and used. It is concluded that the results of the analysis are a reasonable representation of actual flood risk, on the basis of their comparison with historical flood events.     

Correlation of lineaments and groundwater quality in Dasht-e-Arjan Fars,SW of Iran

The NE-oriented Dasht-e-Arjan graben is located 65 km west of Shiraz and has resulted from the active Kare-e-Bas fault segmentations. This extensional graben bounded by two fault system east-Arjan and west-Arjan to the Shahneshin and Salamati anticline. In these study using Landsat 7 ETM images with resolution 2.5 m and directional filtering in the four azimuths and semi-automatic technique for linear structure in the study area. Using the obtained data from extracted lineaments, the rose diagrams of the main strike lineaments are well confirm with field measurements of faults with N56° ± 4°E direction. The structural lineaments of the study area show that the Dasht-e-Arjan area is underlain by the limestone, sandstone, and marl. LANDSAT imagery of the area has been analyzed and interpreted in order to determine the lineament and groundwater quality across the area. The fracture is structurally controlled and mostly influences both the groundwater and surface water pollution and flow directions in the Dasht-e-Arjan. Using visual interpretation, determining the lineaments on the satellite image is very difficult and subjective, and it requires an experienced interpreter. In this study, the lineament analysis is undertaken to examine the orientation of the lineament, the relationship between lineaments and tectonic features and groundwater quality. Lineament density maps show that the lineament density is high around areas. Areas having high lineament density represent areas with relatively high groundwater pollution. Field observations agreed with the results from the analysis of the imagery.     

Geological mapping and mining prospecting in the Aouli inlier (Eastern Meseta,Morocco) based on remote sensing and geographic information systems (GIS)

Landsat 8 Oli, ASTER, and Sentinel 2A satellite images processing was used to map geological formations, lineaments and hydrothermal alteration minerals in the Aouli inlier, as a case study to illustrate the application of digital images processing and Geographic Information System (GIS) in geological mapping and mining prospecting. Principal Component Analysis (PCA) applied to the Landsat images allowed good lithological discrimination and contributed to the updating of available geological maps. The Automatic lineament extraction from Sentinel images revealed the main tectonic structures affecting Aouli inlier. The ratio bands (b5+b7)/b6 and the false color composite (b4/b6, b2/b1, b3/b2) allowed the hydrothermal alteration minerals mapping from Aster images. Combined with available geological data and field observations, the satellite derived data were integrated and analyzed in a GIS software to establish mining prospecting guides. The results showed that the anomaly zones are intimately linked to NNE –SSW and NW –SE oriented faults and to highly fractured areas developing argillic and Fe rich alterations. Verified via field survey, this approach was successfully applied to the Aouli inlier to rapidly target potential areas to be explored in the tactical phase. This provides a model for future prospecting efforts for similar mineral deposits in other areas.©2022 China Geology Editorial Office.     

Landslide susceptibility mapping using random forest,boosted regression tree,classification and regression tree,and general linear models and comparison of their performance at Wadi Tayyah Basin,Asir Region,Saudi Arabia

The purpose of the current study is to produce landslide susceptibility maps using different data mining models. Four modeling techniques, namely random forest (RF), boosted regression tree (BRT), classification and regression tree (CART), and general linear (GLM) are used, and their results are compared for landslides susceptibility mapping at the Wadi Tayyah Basin, Asir Region, Saudi Arabia. Landslide locations were identified and mapped from the interpretation of different data types, including high-resolution satellite images, topographic maps, historical records, and extensive field surveys. In total, 125 landslide locations were mapped using ArcGIS 10.2, and the locations were divided into two groups; training (70 %) and validating (25 %), respectively. Eleven layers of landslide-conditioning factors were prepared, including slope aspect, altitude, distance from faults, lithology, plan curvature, profile curvature, rainfall, distance from streams, distance from roads, slope angle, and land use. The relationships between the landslide-conditioning factors and the landslide inventory map were calculated using the mentioned 32 models (RF, BRT, CART, and generalized additive (GAM)). The models’ results were compared with landslide locations, which were not used during the models’ training. The receiver operating characteristics (ROC), including the area under the curve (AUC), was used to assess the accuracy of the models. The success (training data) and prediction (validation data) rate curves were calculated. The results showed that the AUC for success rates are 0.783 (78.3 %), 0.958 (95.8 %), 0.816 (81.6 %), and 0.821 (82.1 %) for RF, BRT, CART, and GLM models, respectively. The prediction rates are 0.812 (81.2 %), 0.856 (85.6 %), 0.862 (86.2 %), and 0.769 (76.9 %) for RF, BRT, CART, and GLM models, respectively. Subsequently, landslide susceptibility maps were divided into four classes, including low, moderate, high, and very high susceptibility. The results revealed that the RF, BRT, CART, and GLM models produced reasonable accuracy in landslide susceptibility mapping. The outcome maps would be useful for general planned development activities in the future, such as choosing new urban areas and infrastructural activities, as well as for environmental protection.