Landslide Susceptibility Assessment of a Part of the Western Ghats (India) Employing the AHP and F-AHP Models and Comparison with Existing Susceptibility Maps

Landslides are prevalent in the Western Ghats, and the incidences that happened in 2021 in the Koottickal area of the Kottayam district (Western Ghats) resulted in the loss of 10 lives. The objectives of this study are to assess the landslide susceptibility of the high-range local self-governments (LSGs) in the Kottayam district using the analytical hierarchy process (AHP) and fuzzy-AHP (F-AHP) models and to compare the performance of existing landslide susceptible maps. This area never witnessed any massive landslides of this dimension, which warrants the necessity of relooking into the existing landslide-susceptible models. For AHP and F-AHP modeling, ten conditioning factors were selected: slope, soil texture, land use/land cover (LULC), geomorphology, road buffer, lithology, and satellite image-derived indices such as the normalized difference road landslide index (NDRLI), the normalized difference water index (NDWI), the normalized burn ratio (NBR), and the soil-adjusted vegetation index (SAVI). The landslide-susceptible zones were categorized into three: low, moderate, and high. The validation of the maps created using the receiver operating characteristic (ROC) technique ascertained the performances of the AHP, F-AHP, and TISSA maps as excellent, with an area under the ROC curve (AUC) value above 0.80, and the NCESS map as acceptable, with an AUC value above 0.70. Though the difference is negligible, the map prepared using the TISSA model has better performance (AUC = 0.889) than the F-AHP (AUC = 0.872), AHP (AUC = 0.867), and NCESS (AUC = 0.789) models. The validation of maps employing other matrices such as accuracy, mean absolute error (MAE), and root mean square error (RMSE) also confirmed that the TISSA model (0.869, 0.226, and 0.122, respectively) has better performance, followed by the F-AHP (0.856, 0.243, and 0.147, respectively), AHP (0.855, 0.249, and 0.159, respectively), and NCESS (0.770, 0.309, and 0.177, respectively) models. The most landslide-inducing factors in this area that were identified through this study are slope, soil texture, LULC, geomorphology, and NDRLI. Koottickal, Poonjar-Thekkekara, Moonnilavu, Thalanad, and Koruthodu are the LSGs that are highly susceptible to landslides. The identification of landslide-susceptible areas using diversified techniques will aid decision-makers in identifying critical infrastructure at risk and alternate routes for emergency evacuation of people to safer terrain during an exigency

Monitoring and Mapping of Shallow Landslides in a Tropical Environment Using Persistent Scatterer Interferometry: A Case Study from the Western Ghats, India

Persistent Scatterer Interferometry (PSI) techniques are now well established and accepted for monitoring ground displacements. The presence of shallow-seated landslides, ubiquitous phenomena in the tropics, offers an opportunity to monitor and map these hazards using PSI at the regional scale. Thus, the Western Ghats of India, experiencing a tropical climate and in a topographically complex region of the world, provides an ideal study site to test the efficacy of landslide detection with PSI. The biggest challenge in using the PSI technique in tropical regions is the additional noise in data due to vegetation. In this study, we filtered these noises by utilizing the 95-percentile of the highest coherence data, which also reduced the redundancy of the PSI points. The study examined 12 landslides that occurred within one of the three temporal categories grouped as Group 1, Group 2, and Group 3, categorized in relation to PSI monitoring periods, which was also further classified into east- and west-facing landslides. The Synthetic Aperture Radar (SAR) data is in descending mode, and, therefore, the east-facing landslides are characterized by positive deformation velocity values, whereas the west-facing landslides have negative deformation values. Further, the landslide-prone areas, delineated using the conventional factor of safety (FS), were refined and mapped using PSI velocity values. The combination of PSI with the conventional FS approach helped to identify exclusive zones prone to landslides. The main aim of such an attempt is to identify critical areas in the unstable category in the map prepared using FS and prioritizing the mitigation measures, and to develop a road map for any developmental activities. The approach also helps to increase confidence in the susceptibility mapping and reduce false alarms

Application of Analytical Hierarchy Process and Geophysical Method for Groundwater Potential Mapping in the Tata Basin, Morocco

Ensuring water availability for agriculture and drinking water supply in semi-arid mountainous regions requires control of factors influencing groundwater availability. In most cases, the population draws its water needs from the alluvial aquifers close to villages that are already limited and influenced by current climatic change. In addition, the establishment of deep wells in the hard rock aquifers depletes the aquifer. Hence, understanding the factors influencing water availability is an urgent requirement. The use of geographic information system (GIS), and remote sensing (RS), together with decision-making methods like analytical hierarchy process (AHP) will be of good aid in this regard. In the Tata basin, located in SE Morocco, ten factors were used to explain the groundwater potentiality map (GWPM). Five categories of potential zones were determined: very low (8.67%), low (17.74%), moderate (46.77%), high (19.95%), and very high (6.87%). The efficiency of the AHP model is validated using the ROC curve (receiver operating characteristics) which revealed a good correlation between the high potential groundwater zones and the spatial distribution of high flow wells. Geophysical prospecting, using electrical resistivity profiles, has made it possible to propose new well sites. It corresponds to conductive resistivity zones that coincide with the intersection of hydrogeological lineaments

Application of Fuzzy Logic and Fractal Modeling Approach for Groundwater Potential Mapping in Semi-Arid Akka Basin, Southeast Morocco

Groundwater potential delineation in the Akka basin, southwest Morocco, has been determined through the combination of geospatial techniques and geological data. The geometric average and expected value are two multi-criteria approaches used to integrate a set of factors–data for which the weights of each factor are assigned using the fuzzy logic function, which transforms values of factors influencing groundwater presence in a range of [0, 1]. The efficiency factors used in this study are the lineament density, node density, drainage density, distance from rivers, distance from lineament, permeability, slope, topographic witness index, plan curvature, and profile curvature. Thereafter, the groundwater potential map was generated in a GIS environment. To assess and compare the efficiency of the two models, the well data existing in the basin were used to choose the most efficient model. For that reason, the prediction area (P–A) graph, the normalized density (Nd), and its weight (We) were applied to estimate the capacity of each model to predict the target area. The analysis shows that the expected value model (Nd = 1.86 and We = 0.62) is more efficient than the geometric average model (Nd = 0.96 and We = −0.04). The results of the expected value model can be used in the future planning and management of water resources in the Akka basin

Flood Hazard Index Application in Arid Catchments: Case of the Taguenit Wadi Watershed, Lakhssas, Morocco

During the last decade, climate change has generated extreme rainfall events triggering flash floods in short periods worldwide. The delimitation of flood zones by detailed mapping generally makes it possible to avoid human and economic losses, especially in regions at high risk of flooding. The Taguenit basin, located in southern Morocco, is a particular case. The mapping of the flood zones of this basin by the method of the Flood Hazard Index (FHI) in a GIS geographic information systems environment was based on the multi-criteria analysis, taking into consideration the seven parameters influencing these extreme phenomena, namely rainfall, slope, flow accumulation, drainage network density, distance from rivers, permeability, and land use. Average annual rainfall data for 37 years (1980 to 2016) was used in this study for floodplain mapping. A weight was calculated for each parameter using the Analytical Hierarchy Process (AHP) method. The combination of the maps of the different parameters made it possible to draw up a final map classified into five risk intervals: very high, high, moderate, lower and very lower presenting, respectively, 8.04%, 20.63%, 31.47%, 15.36%, and 24.50% of the area of the basin. The reliability of this method was tested by a Flood susceptibility analysis. The results generated by the Flood Hazard Index (FHI) model are similar to those of previous historical events. Realistic and applicable solutions have been proposed to minimize the impact of these floods as much as possible

Wildfire Risk Zone Mapping in Contrasting Climatic Conditions: An Approach Employing AHP and F-AHP Models

Wildfires are one of the gravest and most momentous hazards affecting rich forest biomes worldwide; India is one of the hotspots due to its diverse forest types and human-induced reasons. This research aims to identify wildfire risk zones in two contrasting climate zones, the Wayanad Wildlife Sanctuary in the Western Ghats and the Kedarnath Wildlife Sanctuary in the Himalayas, using geospatial tools, analytical hierarchy process (AHP), and fuzzy-AHP models to assess the impacts of various conditioning factors and compare the efficacy of the two models. Both of the wildlife sanctuaries were severely battered by fires in the past, with more than 100 fire incidences considered for this modeling. This analysis found that both natural and anthropogenic factors are responsible for the fire occurrences in both of the two sanctuaries. The validation of the risk maps, utilizing the receiver operating characteristic (ROC) method, proved that both models have outstanding prediction accuracy for the training and validation datasets, with the F-AHP model having a slight edge over the other model. The results of other statistical validation matrices such as sensitivity, accuracy, and Kappa index also confirmed that F-AHP is better than the AHP model. According to the F-AHP model, about 22.49% of Kedarnath and 17.12% of Wayanad fall within the very-high risk zones. The created models will serve as a tool for implementing effective policies intended to reduce the impact of fires, even in other protected areas with similar forest types, terrain, and climatic conditions

Wildfire Risk Zone Mapping in Contrasting Climatic Conditions: An Approach Employing AHP and F-AHP Models

Wildfires are one of the gravest and most momentous hazards affecting rich forest biomes worldwide; India is one of the hotspots due to its diverse forest types and human-induced reasons. This research aims to identify wildfire risk zones in two contrasting climate zones, the Wayanad Wildlife Sanctuary in the Western Ghats and the Kedarnath Wildlife Sanctuary in the Himalayas, using geospatial tools, analytical hierarchy process (AHP), and fuzzy-AHP models to assess the impacts of various conditioning factors and compare the efficacy of the two models. Both of the wildlife sanctuaries were severely battered by fires in the past, with more than 100 fire incidences considered for this modeling. This analysis found that both natural and anthropogenic factors are responsible for the fire occurrences in both of the two sanctuaries. The validation of the risk maps, utilizing the receiver operating characteristic (ROC) method, proved that both models have outstanding prediction accuracy for the training and validation datasets, with the F-AHP model having a slight edge over the other model. The results of other statistical validation matrices such as sensitivity, accuracy, and Kappa index also confirmed that F-AHP is better than the AHP model. According to the F-AHP model, about 22.49% of Kedarnath and 17.12% of Wayanad fall within the very-high risk zones. The created models will serve as a tool for implementing effective policies intended to reduce the impact of fires, even in other protected areas with similar forest types, terrain, and climatic conditions