Susceptibility Zonation Research Articles

Flood susceptibility mapping using fuzzy analytical hierarchy process for Cedar Rapids, Iowa

ABSTRACT Floods affect over 2.2 billion people worldwide, and their frequency is increasing at an alarming rate compared to other disasters. Presidential disaster declarations have issued increasingly almost every year in Iowa for the past 30 years, indicating that the state is on the rise of flood risk. A multi-disciplinary approach is required, in which the underlying hydrologic processes that cause floods are closely linked with watershed-level socio-economic functions using effective collaboration tools to ensure community participation in the co-production of mitigation plans while paying attention to socio-environmental justice principles. Considering the existing limitations and needs, we conducted a flood risk assessment by utilizing geophysical and socio-economic datasets for a case study in Cedar Rapids, Iowa. Flood risk outputs are generated based on three main risk groups: geophysical-based flood risk, socioeconomic risk, and combined flood risk. An extensive literature review is conducted to determine the pairwise comparison matrices for the parameters that are used in the analytical hierarchy process (AHP) and fuzzy AHP methods. Our results indicate that high- and very-high-risk flood susceptibility zones are primarily located in central urban areas with lower elevations, regardless of the method type (AHP or FAHP). According to overall results, a large area of Cedar Rapids consists of a medium risk level according to the flood risk map combined with the fuzzy AHP method. The results show that high and very high-risk areas are 16% of the studied region, medium, low and very low-risk areas correspond to 84%. Besides, nearly 40% of the population lives in high to very high flood risk zones.

Bipolar intuitionistic fuzzy graph based decision-making model to identify flood vulnerable region.

Bipolar intuitionistic fuzzy graphs (BIFG) are an extension of fuzzy graphs that can effectively capture uncertain or imprecise information in various applications. In graph theory, the covering, matching, and domination problems are benchmark concepts applied to various domains. These concepts may not be defined precisely using a crisp graph when the vertices and edges are more uncertain. Therefore, this study defines the covering, matching and domination concepts in bipolar intuitionistic fuzzy graphs (BIFG) using effective edges with certain important results. To define these concepts when the effective edges are absent, some novel approaches are discussed. To illustrate the domination concepts, the applications in disaster management and location selection problems are discussed. Further, a BIFG-based decision-making model is designed to identify the flood-vulnerable zones in Chennai, where the city's most and least vulnerable zones are identified. From the proposed model, Kodambakkam ([Formula: see text]) is the most susceptible zone in Chennai. Finally, a comparative analysis is done with the existing techniques to show the efficiency of the model.

An ensemble of evidence belief function (EBF) with frequency ratio (FR) using geospatial data for landslide prediction in Darjeeling Himalayan region of India

Landslide is a natural hazards that can be extremely difficult to control without continuous monitoring. The research area is in the Himalayan Mountains region, characterized by complicated geological structures. Landslide occurs in this area and damages property, life, infrastructure, and others. So it is essential to study the susceptible areas for planning and decision-makers. Primarily, 1582 landslides were identified and divided into two distinct groups: the training dataset consisting of 1092 (70%) landslides used to develop the model and the validation dataset consisting of 490 (30%) landslides used to validate the utilized model. In this research, we have used sixteen spatial datasets, viz, topographic datasets are elevation, slope, profile curvature, topographic ruggedness index (TRI), topographic position index (TPI), topographic wetness index (TWI), stream power index (SPI), sediment transport index (STI); hydrological datasets are rainfall and distance to drainage; geological datasets are lithology, geomorphology, distance to fault; landcover datasets are NDVI, distance to road, LULC. This study aims to perform the landslide susceptibility zonation using the performance of evidence belief function (EBF) and frequency ratio (FR) model in the Darjeeling Himalayan region. The susceptibility models have been categorized into five classes, i.e., very low, low, moderate, high, and very high, where EBF model accounting 4.51, 1.22, 25.60, 38.27, and 40.41 percent, respectively, and FR model accounting 5.02, 22.14, 31.61, 33.51, and 7.71 percent, respectively. The receiver operating characteristic and area under the curve (ROC-AUC) was used to measure the success and prediction rates for validation. The result revealed that the success rate of the EBF and FR models is 0.937 and 0.936, and the prediction rate is 0.949 and 0.953, respectively.

Pemetaan Zonasi Kerawanan Bencana Gerakan Massa Tanah di Kabupaten Puncak, Provinsi Papua

The geographical and geomorphological conditions of Puncak Regency has a probability of experiencing the disaster, and based on news, it was found that the mass movements had occurred in Puncak Regency and claimed lives. This research aims to map the mass movement susceptibility zonation in Puncak Regency and analyze the factors that influence it. Method used in this research is a statistical model Frequency Ratio using several variables such as slope, aspect, rock type, lineament density, distance from geological structures, soil texture, distance from rivers, and distance from roads. The model results validated using the Area Under Curve value. The results shows that Puncak Regency dominated by the non-susceptible to moderately susceptible in the northern part, while the Puncak Jaya mountains has a high-very high susceptibility class. The most influential variables on the occurrence of mass movement are rock type, lineament density, and slope. The validation results show that the results of the mass movement susceptibility model is statistically acceptable.

Landslide Susceptibility Zoning in Yunnan Province Based on SBAS-InSAR Technology and a Random Forest Model

Yunnan Province, China, has complex topography and geomorphology, many ravines and valleys, and frequent landslide geological disasters and is of great significance in the assessment of regional landslide geological disasters in Yunnan Province for disaster prevention and mitigation. In this study, Yunnan Province was selected as the research area, and the average annual deformation rate of radar line-of-sight in Yunnan Province over four years from 2018 to 2021 was obtained with SBAS-InSAR technology, which was used as one of the index factors for the susceptibility evaluation of Yunnan Province. The deformation rate reflects the slow movement of the land surface. In addition, elevation, slope, aspect, lithological classification, geological structure, rainfall, distance from roads, distance from rivers, topographic undulation, and NDVI were selected as evaluation index factors and combined with the annual mean deformation rate. A random forest model was used to evaluate and accurately analyze landslide geological disasters in Yunnan Province. The results showed that as an important index factor, the annual mean deformation rate of Yunnan Province can be added to the random forest model to improve the prediction accuracy. The area with high susceptibility accounted for 10% of the entire province, and the number of landslides in the region accounted for 68% of the province. Additionally, the results for prone zoning were highly correlated with the landslide distribution. The accuracy of the random forest model prediction was 0.80, and the AUC value was 0.87, indicating that the random forest model was a highly accurate and reliable evaluation method for studying landslide geological disasters. It is very suitable for the evaluation of landslide susceptibility in Yunnan Province.

Landslide susceptibility prediction using frequency ratio model: a case study of Uttarakhand, Himalaya (India)

The purpose of this study is to develop a landslide susceptibility prediction model by applying the Frequency Ratio (FR) model and remote sensing data sets for the Northern part of Uttarakhand, India. First, a landslide inventory was carried out from the interpretation of satellite images. Thereafter, the landslide inventory points were randomly separated into training and validation datasets. Subsequently, the significant landslide causative factors such as slope, lithology, lineament density, land use/land cover, drainage density, aspect, elevation, road buffer, normalized differential vegetation index (NDVI), stream power index, and topographic wetness index were identified to run the model set up. Next, applying the FR statistical model in a GIS environment for development of landslide susceptibility index map and divided into five distinct landslide susceptibility zones (very low, low, moderate, high, and very high). To validate the results, the Receiver Operating Characteristics (ROC) curve were developed to check the accurrancy of the model, and it was observed that the prediction value of the FR model was reasonably accurate (86.1% at 95% confidence level). The output LSI map would be helpful for the government and planners to map and monitor potential landslide areas and mitigate the hazards.

GIS-based modeling of landslide susceptibility zonation by integrating the frequency ratio and objective–subjective weighting approach: a case study in a tropical monsoon climate region

Accurate detection of landslide spatial patterns is vital in susceptibility, hazard, and risk disaster mapping. Geographic Information System (GIS)-based quantitative approaches provide a rigorous procedure for gaining deep insight into natural and anthropogenic landslides from different scales. This study aims to implement a comprehensive solution for retrieving the landslide susceptibility index. For that purpose, a landslide inventory was performed in a tropical monsoon climate region, with a magnitude of elevation spanning from −65 m to 1,900 m above the sea, considering 15 fundamental causative factors belonging to the groups of topography, hydrology, geology, land cover conditions and anthropogenic activities, and weather. The frequency ratio (FR) was implemented to rank subclasses in each causative factor. For factor weight estimation, different approaches were applied, including the subjective-based analytic hierarchy process (AHP), objective-based Shannon entropy (SE), and a synergy of both methods (AHP–SE), built on these two approaches. Out of the 271 identified landslide locations, 70% (196 points) were used for training and the remaining 30% (71 points) were applied for validation. The results showed that the integrated AHP–SE outperformed the two individual approaches, with the area under the receiver operating characteristic curve (AUC) reaching 0.876, following SE (AUC = 0.848) and AHP (AUC = 0.818). In the synergy approach, the climate pattern under tropical monsoons was confirmed as the most crucial landslide-predisposing factor. The research contributes to a novel discussion by integrating knowledge-based consultation and statistical data analysis of accurate geospatial data, incorporating significant explanatory factors toward a reliable landslide-prone zonation over space and time dimensions.

Development Pattern of Toppling Deformation Slopes in Western China

Controlled by regional geological background, and affected by human activity and weathering, toppling deformation slopes occur in different regions in China. Based on statistics about domestic toppling deformation slopes and their distribution, the regional geological susceptibility zonation area of toppling deformation slopes was determined. Firstly, a regional geological susceptibility evaluation was conducted according to these toppling deformation slopes, choosing landform, stratigraphic age, seismic intensity and tectonic stress distribution, and the study area was divided into very low, low, medium, high and very high susceptibility areas. Secondly, a geographic position susceptibility evaluation was conducted aimed at different sections of typical rivers in western China and the toppling deformation slopes related to them, and the study area was divided into upstream, midstream and downstream of 11 rivers that were respectively classified into very low, low, high and very high susceptibility areas. Thirdly, based on the results, associations between the toppling deformation factors (lithology, slope height, elevation, horizontal depth and vertical depth) and the distribution of deformation slopes were revealed. Finally, the development regularity was analyzed comprehensively. The results show that the toppling deformation slopes of hydropower projects are more developed in Sichuan, Qinghai and Yunnan provinces of China. With regard to regional geography, besides the Yellow River, both the Lancang River and the Yalong River flow through areas with toppling deformation that are classified as highly susceptible geological zones in all levels. The results of this study can be used as references for site selection plans in hydropower projects.

Artificial neural network for flood susceptibility mapping in Bangladesh

The objective of the research is to investigate flood susceptibility in the Sylhet division of Bangladesh. Eight influential factors (i.e., elevation, slope, aspect, curvature, TWI, SPI, roughness, and LULC) were applied as inputs to the model. In this work, 1280 samples were taken at different locations based on flood and non-flood characteristics; of these, 75% of the inventory dataset was used for training and 25% for testing. An artificial neural network was applied to develop a flood susceptibility model, and the results were plotted on a map using ArcGIS. According to the finding, 40.98% (i.e., 499433.50 hectors) of the study area is found within the very high-susceptibility zone, and 37.43% (i.e., 456168.76 hectors) are in the highly susceptible zone. Only 6.52% and 15% of the area were found in low and medium flood susceptibility zones, respectively. The results of model validation show that the overall prediction rate is around 89% and the overall model success rate is around 98%. The study's findings assist policymakers and concerned authorities in making flood risk management decisions in order to mitigate the negative impacts.

Geospatial-Based Analytical Hierarchy Process (AHP) and Weighted Product Model (WPM) Techniques for Mapping and Assessing Flood Susceptibility in the Wadi Hanifah Drainage Basin, Riyadh Region, Saudi Arabia

This paper aimed to map areas prone to flooding in the Wadi Hanifah drainage basin located in the Riyadh region, and identify the most important factors that contribute to flooding through examining the influence of ten topographical, hydrological, and environmental variables affecting flood occurrence. Remote sensing data from Landsat-8, Shuttle Radar Topography Mission (SRTM), and other ancillary datasets were used to map relevant variables. Two weighted overlay techniques were used, including: analytical hierarchy process (AHP) and weighted product model (WPM). A correlation matrix and optimum index factor (OIF) were employed to identify the relative importance of each factor. The two derived flood susceptibility maps were assessed through validation by comparing the locations of historical flood events to susceptibility zones. The results confirmed the validity of the WPM map. The results also showed that nearly 50% of the study area was dominated by the “moderate” flood susceptibility zone, while about 33% of the total land area was classified as a “high” flood susceptibility zone. The “slope” factor was found to be the most effective variable for flood occurrence, followed by the “geology” variable, while the “distance to the drainage network” was the least important variable. The results of the OIF indicated that the best combination of factors dictating the variability of all flood susceptibility areas were “geology”, “land use/cover (LULC)”, and “soil type”. The study findings are expected to be useful in understanding the effects of each factor on the spatial variation in flood occurrence and in improving flood control, and can be reapplied to other regions with similar climatic and environmental conditions worldwide.

A hybrid machine learning model for landslide-oriented risk assessment of long-distance pipelines

Preparation of pipeline risk zoning is essential for pipeline construction and safe operation. Landslides are one of the main sources of risk to the safe operations of oil and gas pipelines in mountainous areas. This work aims to propose a quantitative assessment model of landslide-induced long-distance pipeline risk by analyzing historical landslide hazard data along oil and gas pipelines. Using the Changshou-Fuling-Wulong-Nanchuan (CN) gas pipeline dataset, two independent assessments were carried out: landslide susceptibility assessment and pipeline vulnerability assessment. Firstly, the study combined the recursive feature elimination and particle swarm optimization-AdaBoost method (RFE-PSO-AdaBoost) to develop a landslide susceptibility mapping model. The RFE method was used to select the conditioning factors, while PSO was used to tune the hyper-parameters. Secondly, considering the angular relationship between the pipelines and landslides, and the segmentation of the pipelines using the fuzzy clustering (FC), the CRITIC method (FC-CRITIC) was combined to develop a pipeline vulnerability assessment model. Accordingly, a pipeline risk map was obtained based on pipeline vulnerability and landslide susceptibility assessment. The study results show that almost 35.3% of the slope units were in extremely high susceptibility zones, 6.68% of the pipelines were in extremely high vulnerability areas, the southern and eastern pipelines segmented in the study area were located in high risk areas and coincided well with the distribution of landslides. The proposed hybrid machine learning model for landslide-oriented risk assessment of long-distance pipelines can provide a scientific and reasonable risk classification for new planning or in service pipelines to avoid landslide-oriented risk and ensure their safe operation in mountainous areas.

GIS-based machine learning algorithm for flood susceptibility analysis in the Pagla river basin, Eastern India

The unique characteristics of drainage conditions in the Pagla river basin cause flooding and harm the socioeconomic environment. The main purpose of this study is to investigate the comparative utility of six machine learning algorithms to improve flood susceptibility and ensemble techniques' capability to elucidate the underlying patterns of floods and make a more accurate prediction of flood susceptibilities in the Pagla river basin. In the present scenario, the frequency of flood conditions in this study area becomes high with heavy and sudden rainfall, so it is essential to study flood mitigation and measure. At first, a spatial flood database was built with 200 flood locations and sixteen flood influencing factors, and its process with the help of the Geographic Information System (GIS) environment and build up different models applying the machine learning techniques. It has found different flood susceptibility zone using machine learning-based Artificial Neural Network (ANN), Support Vector Machine (SVM), Random Forest (RF), Reduced Error Pruning Tree (REPTree), Logistic Regression (LR), and Bagging helping GIS environment and the model validation using the Receiver Operating Characteristic (ROC) Curve. Afterward, ensemble all the models to gate comparative accuracy of the flood zone. The calculated areas are under the very high flood susceptibility zone 8.69%, 14.92%, 14.17%, 12.98%, 14.65%, 13.24% and 13.41% for ANN, SVM, RF, REPTree, LR and Bagging, respectively. Finally, ROC curve, the Standard Error (SE), and the Confidence Interval (CI) at 95 per cent were used to assess and compare the performance of the models. The obtained results indicate that all models are highly accepted Area Under Curve (AUC) of ROC between 0.889 (LR) to 0.926 (Ensemble). From the estimation of the accuracy of the applied methods using ROC, it is found that the Ensemble model has the higher capability compared to the other applied models in projecting flood susceptibility in ​the ​study ​area. It has the highest area under the ROC curve the AUC values are 0.918 and 0.926, the SE (0.023, 034), and the narrowest CI (95 per cent) (0.873–0.962, 0.859–0.993) whereas highest area under Bagging (the ROC) curve (AUC) value (0.914, 0.919), for both the training and validation datasets. After ensembling, the result shows that the result is a highly flood susceptible area located at the lower part of the study area. In this area, the very high flood susceptibility zone values lie between 4.46 and 6.00 in the ensemble result. The areas comprise the low height and belong to Murarai I, Murarai II, Suti I and Suti II C.D. block of West Bengal. The current study will help the policymakers and the researcher determine the flood conditioning problems for prospects.

Determination of Susceptibility to the Generation of Discontinuities Related to Land Subsidence Using the Frequency Ratio Method in the City of Aguascalientes, Mexico

Land subsidence in the Aguascalientes Valley, documented since the 1980s, has developed a large number of discontinuities that damage infrastructure. There is currently no methodology to accurately predict the site and time at which a discontinuity will occur, making it difficult to make decisions in urban planning or risk management. However, it is possible to determine the susceptibility of an area to the generation of fractures based on the factors associated with their formation. This study presents a zoning method based on the ground failure susceptibility index (GFSI) in the city of Aguascalientes, using the frequency ratio (FR) method and employing the depth of the basement, the subsidence rate, the subsidence gradient, and the groundwater level drawdown as variables. The zoning method included three categories of land subsidence susceptibility to fracturing, moderate, high, and very high, which were divided using the first (3.76) and second (4.24) quartiles of the GFSI. The zoning method was created with the discontinuities reported in 2010 and was validated with data from 2022. The results obtained show that 11.19% of the discontinuities developed between 2010 and 2022 were located in a zone of moderate susceptibility, 41.97% were located in a zone of high susceptibility, and 46.87% were located in a zone classified as having very high susceptibility.

Geospatial Modeling Based-Multi-Criteria Decision-Making for Flash Flood Susceptibility Zonation in an Arid Area

Identifying areas susceptible to flash flood hazards is essential to mitigating their negative impacts, particularly in arid regions. For example, in southeastern Sinai, the Egyptian government seeks to develop its coastal areas along the Gulf of Aqaba to maximize its national economy while preserving sustainable development standards. The current study aims to map and predict flash flood prone areas utilizing a spatial analytic hierarchy process (AHP) that integrates GIS capabilities, remote sensing datasets, the NASA Giovanni web tool application, and principal component analysis (PCA). Nineteen flash flood triggering parameters were initially considered for developing the susceptibility model by conducting a detailed literature review and using our experiences in the flash food studies. Next, the PCA algorithm was utilized to reduce the subjective nature of the researchers’ judgments in selecting flash flood triggering factors. By reducing the dimensionality of the data, we eliminated ten explanatory variables, and only nine relatively less correlated factors were retained, which prevented the creation of an ill-structured model. Finally, the AHP method was utilized to determine the relative weights of the nine spatial factors based on their significance in triggering flash floods. The resulting weights were as follows: rainfall (RF = 0.310), slope (S = 0.221), drainage density (DD = 0.158), geology (G = 0.107), height above nearest drainage network (HAND = 0.074), landforms (LF = 0.051), Melton ruggedness number (MRN = 0.035), plan curvature (PnC = 0.022), and stream power index (SPI = 0.022). The current research proved that AHP, among the most dependable methods for multi-criteria decision-making (MCDM), can effectively classify the degree of flash flood risk in ungauged arid areas. The study found that 59.2% of the area assessed was at very low and low risk of a flash flood, 21% was at very high and high risk, and 19.8% was at moderate risk. Using the area under the receiver operating characteristic curve (AUC ROC) as a statistical evaluation metric, the GIS-based AHP model developed demonstrated excellent predictive accuracy, achieving a score of 91.6%.

Ensemble of fuzzy-analytical hierarchy process in landslide susceptibility modeling from a humid tropical region of Western Ghats, Southern India.

The western flanks of the Western Ghats are one of the major landslide hotspots in India. Recent rainfall triggered landslide incidents in this humid tropical region necessitating the accurate and reliable landslide susceptibility mapping (LSM) of selected parts of Western Ghats for hazard mitigation. In this study, a GIS-coupled fuzzy Multi-Criteria Decision Making (MCDM) technique is used to evaluate the landslide-susceptible zones in a highland segment of the Southern Western Ghats. Fuzzy numbers specified the relative weights of nine landslide influencing factors that were established and delineated using the ArcGIS, and the pairwise comparison of these fuzzy numbers in the Analytical hierarchy process (AHP) system resulted in standardized causative factor weights. Thereafter, the normalized weights are assigned to corresponding thematic layers, and finally, a landslide susceptibility map is generated. The model is validated using the area under the curve values (AUC) and F1 scores. The result reveals that about 27% of the study area is classified as highly susceptible zones followed by 24% area in moderately susceptible zone, 33% in low susceptible, and 16% in a very low susceptible area. Also, the study shows that the plateau scarps in the Western Ghats are highly susceptible to the occurrence of landslides. Moreover, the predictive accuracy estimated by the AUC scores (79%) and F1 scores (85%) shows that the LSM map is trustworthy for future hazard mitigation and land use planning in the study area.

Settlement Analysis in Potentially Liquifiable Soil at Kretek 2 Bridge Area

The Kretek 2 Bridge is one of the important infrastructures built on the southern coast road of Java. Referring to the Map of Indonesia Liquefaction Vulnerability Zone in 2019, Kretek 2 Bridge was identified as a high liquefaction susceptibility zone. The result of liquefaction potential analysis on the slab-on-pile section indicates that the soil layer has potential to liquefy if an earthquake occurs. Based on the Liquefaction Severity Index, the slab-on-pile section of the Kretek 2 Bridge has a high severity index at BH-10, moderate severity index at BH-11, and low severity index at BH-01 and BH-12. One of liquefaction effects is a ground settlement. Settlement due to liquefaction is a vertical soil deformation in the soil layer caused by compaction due to the earthquake. This study was conducted by calculating the settlement of sand deposits in slab-on-pile section using an equation proposed by Zhang et al. in 2002 and Yoshimine in 2006. The results of settlement analysis using Zhang’s method to predict settlement in the study area, BH-10 having the most significant settlement at 29.803 cm. Using the settlement analysis method by Yoshimine, the highest settlement found in BH-10, which is 30.401 cm.

Susceptibility Zoning of Soil Movement in Tawangmangu District, Karanganyar Regency with Bivariate Statistic Method - Weight of Evidence

Tawangmangu district which is located in Karanganyar Regency, Central Java is a crowded area which is susceptible to disaster. Therefore, susceptibility zoning analysis is needed to support the planning and development of the area. The method used is bivariate statistics - weight of evidence which has never been implemented in its location. There are two types of data, data of potential and soil movement case (divided into train data as 61 points and test data as 40 points); and parameter data of soil movement causes. Parameter data of soil movement is processed to be a WoE (Weight of Evidence) parameter map through the use of train data to know the weight. Later on, it will be validated based on the AUC value. If it’s > 0,60, then the process will proceed. Parameter which has AUC value > 0,60 is type of lithology, slope, hill, elevation, slope direction, distance from the fault, and index of vegetation density. All of those parameters are attached and being final validated by test data. Further, the zonation consists of four; zone of very low soil movement susceptibility, low, medium, and high. The area of the very low soil movement susceptibility zone is 19,68%. Zone of low susceptibility is mostly 24,57%. Zone of medium susceptibility is 25,88%. Meanwhile, the zone of high susceptibility is 29,86%. The result of final validation shows that AUC value from the zoning model made belongs to a good category, which is 0,757.

A Multi-Criteria Decision Analysis (MCDA) Approach for Landslide Susceptibility Mapping of a Part of Darjeeling District in North-East Himalaya, India

Landslides are the nation’s hidden disaster, significantly increasing economic loss and social disruption. Unfortunately, limited information is available about the depth and extent of landslides. Therefore, in order to identify landslide-prone zones in advance, a well-planned landslide susceptibility mapping (LSM) approach is needed. The present study evaluates the efficacy of an MCDA-based model (analytical hierarchy process (AHP)) and determines the most accurate approach for detecting landslide-prone zones in one part of Darjeeling, India. LSM is prepared using remote sensing thematic layers such as slope, rainfall earthquake, lineament density, drainage density, geology, geomorphology, aspect, land use and land cover (LULC), and soil. The result obtained is classified into four classes, i.e., very high (11.68%), high (26.18%), moderate (48.87%), and low (13.27%) landslide susceptibility. It is observed that an entire 37.86% of the area is in a high to very high susceptibility zone. The efficiency of the LSM was validated with the help of the receiver operating characteristics (ROC) curve, which demonstrate an accuracy of 96.8%, and the success rate curve showed an accuracy of 81.3%, both of which are very satisfactory results. Thus, the proposed framework will help natural disaster experts to reduce land vulnerability, as well as aid in future development.

Land Subsidence Susceptibility Mapping Using Interferometric Synthetic Aperture Radar (InSAR) and Machine Learning Models in a Semiarid Region of Iran

Most published studies identify groundwater extraction as the leading cause of land subsidence (LS). However, the causes of LS are not only attributable to groundwater extraction. Other land-use practices can also affect the occurrence of LS. In this study, radar interferometric techniques and machine learning (ML) models were used for the prediction, susceptibility zoning, and prioritization of influential variables in the occurrence of LS in the Bakhtegan basin. The LS rate was characterized by applying an interferometric synthetic aperture radar (InSAR). The recursive feature elimination (RFE) method was used to detect and select the dominant combination of indicators to prepare an LS susceptibility map. Three ML models, including random forest (RF), k-nearest neighbors (KNN), and classification and regression trees (CART), were used to develop predictive models. All three models had acceptable performance. Among the ML models, the RF model performed the best (i.e., Nash–Sutcliffe efficiency, Kling–Gupta efficiency, correlation coefficient, and percent bias metrics of 0.76, 0.78, 0.88, and 0.70 for validating phase, respectively). The analysis conducted on all three ML model outputs showed that high and very high LS susceptibility classes were located on or near irrigated agricultural land. The results indicate that the leading cause of land LS in the study region is not due to groundwater withdrawals. Instead, the distance from dams and the proximity to anticlines, faults, and mines are the most important identifiers of LS susceptibility. Additionally, the highest probability of LS susceptibility was found at distances less than 18 km from synclines, 6 to 13 km from anticlines, 23 km from dams, and distances less than 20 to more than 144 km from mines. The validated methods presented in this study are reproducible, transferrable, and recommended for mapping LS susceptibility in semiarid and arid climate zones with similar environmental conditions.

GIS based landslide susceptibility mapping and assessment using weighted overlay method in Wayanad: A part of Western Ghats, Kerala

The objectives of the study are to generate a landslide susceptibility zonation (LSZ) map of Wayanad for effective and efficient disaster management in the future and to determine the various parameters and usefulness of remote sensing and GIS in landslide susceptibility mapping. The topographical factors like slope, elevation, and aspects influencing landslides are derived from the digital elevation model (DEM). The rainfall data were collected from 2011 to 2019 from the Indian Meteorological Department (IMD) and four sampling sites were identified through a field visit. We used Landsat8 Enhanced Thematic Mapper Plus (ETM+) satellite imagery with a resolution of 30 m to create land use and land cover (LULC) using the unsupervised classification method. It was divided into seven categories based on the annual precipitation of the study area using inverse distance weighting (IDW). The LSZ map was derived using the weighted overlay method by analysing the geographical and climatic factors. These factors helped produce the final LSZ map, which was divided into three zones from high to low. LSZ areas with steep slopes, high elevation, prolonged rainfall, and predominant land use changes. Study reveals that the north-east, south, and south-east regions of the study area are more vulnerable to landslides.