Comparison between prediction capabilities of neural network and fuzzy logic techniques for L and slide susceptibility mapping.

Preparation of L and slide susceptibility maps is important for engineering geologists and geomorphologists. However, due to complex nature of L and slides, producing a reliable susceptibility map is not easy. In recent years, various data mining and soft computing techniques are getting popular for the prediction and classification of L and slide susceptibility and hazard mapping. This paper presents a comparative analysis of the prediction capabilities between the neural network and fuzzy logic model for L and slide susceptibility mapping in a geographic information system (GIS) environment. In the first stage, L and slide-related factors such as altitude, slope angle, slope aspect, distance to drainage, distance to road, lithology and normalized difference vegetation index (ndvi) were extracted from topographic and geology and soil maps. Secondly, L and slide locations were identified from the interpretation of aerial photographs, high resolution satellite imageries and extensive field surveys. Then L and slide-susceptibility maps were produced by the application of neural network and fuzzy logic approahc using the aforementioned L and slide related factors. Finally, the results of the analyses were verified using the L and slide location data and compared with the neural network and fuzzy logic models. The validation results showed that the neural network model (accuracy is 88%) is better in prediction than fuzzy logic (accuracy is 84%) models. Results show that "gamma" operator (X = 0.9) showed the best accuracy (84%) while "or" operator showed the worst accuracy (66%)

Hydro-Chemical analysis of the ground water of the Basaltic Catchments: Upper Bhatsai Region, Maharastra.

Water being an excellent solvent tends to dissolve the minerals in the geological system. The chemical nature of the ground water is influenced by several factors such as chemical weathering of the country rocks and interaction with the country rocks. The importance of the hydrochemical analysis underlies the fact that the chemistry of the ground water can directly be rated with the source of water, climate, and geology of the region. In this paper chemical analysis of the ground water has been carried out for upper Bhatsai region in Maharastra. There are eight water quality variables (SO42-, Na++, K+, Mg2+, Ca2+, NO3-, TH, and pH) and the specific Conductance and Total Dissolved Solids were selected for this analysis. In this paper a) The values of water quality parameters were analyzed using statistical methods, b) the existence of trends and the evaluation of the best-fitted models were performed in order to classify the quality of the ground water. The geochemical analysis of the water samples has shown that it is free from certain anomalies and the water is suitable for human and cattle consumption. However, the presence of certain degree of anions indicates that the ground water in the study area is facing stress which could change the quality of the water in the near future

Landslide hazard and risk analyses at a landslide prone catchment area using statistical based geospatial model.

This paper presents the application of remote sensing techniques, digital image analysis and Geographic Information System tools to delineate the degree of landslide hazard and risk areas in the Balik Pulau area in Penang Island, Malaysia. Its causes were analysed through various thematic attribute data layers for the study area. Firstly, landslide locations were identified in the study area from the interpretation of aerial photographs, satellite imageries, field surveys, reports and previous landslide inventories. Topographic, geologic, soil and satellite images were collected and processed using Geographic Information System and image processing tools. There are 12 landslide-inducing parameters considered for the landslide hazard analyses. These parameters are: topographic slope, topographic aspect, plan curvature, distance to drainage and distance to roads, all derived from the topographic database; geology and distance to faults, derived from the geological database; landuse/landcover, derived from Landsat satellite images; soil, derived from the soil database; precipitation amount, derived from the rainfall database; and the vegetation index value, derived from SPOT satellite images. In addition, hazard analyses were performed using landslide-occurrence factors with the aid of a statistically based frequency ratio model. Further, landslide risk analysis was carried out using hazard map and socio-economic factors using a geospatial model. This landslide risk map could be used to estimate the risk to population, property and existing infrastructure like transportation networks. Finally, to check the accuracy of the success-rate prediction, the hazard map was validated using the area under curve method. The prediction accuracy of the hazard map was 89%. Based on these results the authors conclude that frequency ratio models can be used to mitigate hazards related to landslides and can aid in land-use planning

Land surface temperature assessment in semi-arid residential area of Tehran, Iran using Landsat imagery

Land cover change especially from green areas to urban areas may increase land surface temperature (LST). In this study, Landsat Enhanced Thematic Mapper Plus (ETM+) on 15 May 2000 (spring), 9 July 2000 (summer), 26 November 2000 (autumn) and 10 January 2001(winter) were utilized to study LST in Tehran, Iran. The accuracy of the LST analysis was evaluated using six year ground temperature data. The Non Linear Correlation Coefficient (NLCC) between normalized differences vegetation index (NDVI) and LST was found to be higher in the spring compared to the other seasons. The LST value in the west of the city was similar to the surrounding areas, but in north, east and south of the city were lower compared to the north, north east and east of the surrounding areas in all seasons. The gravel and sandy soil in the western part of the surrounding areas were warmer than the impervious surface area (ISA) in the city in summer. It was found that high urban density in semi arid climate with low vegetation in the surrounding areas does not increase the LST value in the city compared to its surrounding areas

Enhancement of semi-automated lineament extraction from IRS- 1B satellite images for part of Himalayan region

This paper presents the results of a spatial domain filtering investigation of lineament mapping from IRS- 1B LISS- I satellite image. A quick and accurate lineament extraction method is applied to a big IRS-1B scene of the study area. Further, the orientation and structural trend of the area is also discussed with respect to the derived lineaments. Efforts have been made to evaluate the techniques as a fast algorithm for quick and time limited analysis of lineaments from which their orientations are estimated. To achieve the objective, various filtering techniques have been used for extraction of the lineaments form IRS-1B scene. In the present study, the acquired IRS-1B satellite scene after being geocoded, has been divided into twelve equal sized windows and a separate raster layer has been made for each of the windows. Two computer programs were used for preparation of the data sets and plotting of the rose diagrams. The result demonstrated that the lineament density value is relatively higher in the high relief area which indicates the presence of fractured rocks with abundant joints and faults owing to the structurally active terrain. As a conclusion, the current method has been found to be useful for lineament extraction from a complex terrain

The ASTER DEM generation for geomorphometric analysis of the Central Alborz mountains, Iran

This research focuses on the ASTER DEM generation for visual and mathematical analysis of topography, landscapes and landforms, as well as modeling of surface processes of Central Alborz, Iran. ASTER DEM 15 m generated using tie points over the Central Alborz and Damavand volcano with 5671 m height from ASTER (Advanced Space borne Thermal Emission and Reflection Radiometer) satellite data using PCI Geomatica 9.1. Geomorphic parameters are useful to identify and describe geomorphologic forms and processes, which were extracted from ASTER DEM in GIS environment such as elevation, aspect, slope angle, vertical curvature, and tangential curvature. Although the elevation values are slightly low in altitudes above 5500 m asl., the ASTER DEM is useful in interpretation of the macro- and meso-relief, and provides the opportunity for mapping especially at medium scales (1:100,000 and 1:50,000). ASTER DEM has potential to be a best tool to study 3D model for to geomorphologic mapping and processes of glacial and per glacial forms above 4300 m asl

Landslides and active faults using remote sensing and GIS techniques in Central Alborz mountains, Iran

The attempt has been done on study of habitat factors for the distribution of 370 landsides. This study is based on landform features and landslides associated with active faults distribution using remote sensing, GIS and GPS techniques in the Central Alborz, North Iran. Field observations show that the mass movements on low angle occur most frequently near to active faults. In steep slopes avalanche and planar slides are dominant. In this study digital image processing has been done on the ASTER L1A, L1B and Landsat7 ETM+ images. GIS layers have been extracted from 370 historical landslides and active faults over the study area. Digital Elevation Model (DEM) (15m) has been generated from ASTER stereo pair data using PCI Geomatica 9.1 software. The use of a (15m) DEM is a potential substitute in tectonic activity analysis, as it highly correlates with slope instability, geomorphologic processes and factors affecting landslides. Appropriate landform parameters have been derived which are indicating landslides and faults distribution, exposure towards rain and snow. Tectonic classification schemes decomposing the landscape into basic landform-elements proved useful for characterizing a zonal, altitudinal landslide classes. The results show that more than 72 percent of landslide points are situated on the active faults buffer zone. It can be used as fundamental data for hazard prediction, land use planning and construction in study area

Hybridizing genetic random forest and self-attention based CNN-LSTM algorithms for landslide susceptibility mapping in Darjiling and Kurseong, India

Landslides are a prevalent natural hazard in West Bengal, India, particularly in Darjeeling and Kurseong, resulting in substantial socio-economic and physical consequences. This study aims to develop a hybrid model, integrating a Genetic-based Random Forest (GA-RF) and a novel Self-Attention based Convolutional Neural Network and Long Short-term Memory (SA-CNN-LSTM), for accurate landslide susceptibility mapping (LSM) and generate landslide vulnerability-building map in these regions. To achieve this, we compiled a database with 1830 historical data points, incorporating a landslide inventory as the dependent variable and 32 geo-environmental parameters from Remote Sensing (RS) and Geographic Information Systems (GIS) layers as independent variables. These parameters include features like topography, climate, hydrology, soil properties, terrain distribution, radar features, and anthropogenic influences. Our hybrid model exhibited superior performance with an AUC of 0.92 and RMSE of 0.28, outperforming standalone SA-CNN-LSTM, GA-RF, RF, MLP, and TreeBagger models. Notably, slope, Global Human Modification (gHM), Combined Polarization Index (CPI), distances to streams and roads, and soil erosion emerged as key layers for LSM in the region. Our findings identified around 30% of the study area as having high to very high landslide susceptibility, 20% as moderate, and 50% as low to very low. The vulnerability-building map for 244,552 building footprints indicated varying landslide risk levels, with a significant proportion (27.74%) at high to very high risk. Our model highlighted high-risk zones along roads in the northeastern and southern areas. These insights can enhance landslide risk management in Darjeeling and Kurseong, guiding sustainable strategies for future damage qualification

Solving water scarcity challenges in arid regions: A novel approach employing human-based meta-heuristics and machine learning algorithm for groundwater potential mapping

Addressing water scarcity challenges in arid regions is a pressing concern and demands innovative solutions for accurate groundwater potential mapping (GPM). This study presents a comprehensive evaluation of advanced modeling techniques to enhance the precision of GPM. This study, conducted in the Zayandeh Rood watershed, Iran, employed a spatial database comprising 16 influential factors on groundwater potential and data from 175 wells. This study introduced an innovative approach to GPM by enhancing the Random Forest (RF) algorithm. This enhancement involved integrating three metaheuristic algorithms inspired by human behavior: ICA (Imperialist Competitive Algorithm), TLBO (Teaching-Learning-Based Optimization), and SBO (Student Psychology Based Optimization). The modeling process used 70% training data and 30% evaluation data. Data preprocessing was performed using the multicollinearity test method and frequency ratio (FR) technique to refine the dataset. Subsequently, the GPM was generated using four distinct models, demonstrating the combined power of machine learning and human-inspired metaheuristic algorithms. The performance of the models was systematically assessed through extensive statistical analyses, including root mean squared error (RMSE), mean absolute error (MAE), area under the curve (AUC) for the receiver operating characteristic curve (ROC), Friedman tests, chi-squared tests, and Wilcoxon signed-rank tests. RF-ICA and RF-SPBO emerged as frontrunners, displaying statistically comparable accuracy and significantly outperforming RF-TLBO and the non-optimized RF model. The results of the GPM revealed the exceptional accuracy of RF-ICA, which exhibited a commanding AUC score of 0.865, underscoring its superiority in discriminating between different groundwater potential classes. RF-SPBO also displayed strong performance with an AUC of 0.842, highlighting its effectiveness in inaccurate classification. RF-TLBO and the non-optimized RF model achieved AUC values of 0.813 and 0.810, respectively, indicating comparable performance. The outcomes of this study provide valuable insights for policymakers, offering a robust framework for tackling water scarcity challenges in arid regions through precise and reliable groundwater potential assessments