Development and Assessment of GIS-Based Landslide Susceptibility Mapping Models Using ANN, Fuzzy-AHP, and MCDA in Darjeeling Himalayas, West Bengal, India

Landslides, a natural hazard, can endanger human lives and gravely affect the environment. A landslide susceptibility map is required for managing, planning, and mitigating landslides to reduce damage. Various approaches are used to map landslide susceptibility, with varying degrees of efficacy depending on the methodology utilized in the research. An analytical hierarchy process (AHP), a fuzzy-AHP, and an artificial neural network (ANN) are utilized in the current study to construct maps of landslide susceptibility for a part of Darjeeling and Kurseong in West Bengal, India. On a landslide inventory map, 114 landslide sites were randomly split into training and testing with a 70:30 ratio. Slope, aspect, profile curvature, drainage density, lineament density, geomorphology, soil texture, land use and land cover, lithology, and rainfall were used as model inputs. The area under the curve (AUC) was used to examine the models. When tested for validation, the ANN prediction model performed best, with an AUC of 88.1%. AUC values for fuzzy-AHP and AHP are 86.1% and 85.4%, respectively. According to the statistics, the northeast and eastern portions of the study area are the most vulnerable. This map might help development in the area by preventing human and economic losses

Evaluating evolutionary algorithms for simulating catchment response to river discharge

Evolutionary algorithms (EAs) are proficient in solving the controlled, nonlinear multimodal, non-convex problems that limit the use of deterministic approaches. The competencies of EA have been applied in solving various environmental and water resources problems. In this study, the storm water management model (SWMM) was set up to authenticate the capability of the model for simulating catchment response in the upper Damodar River basin. Auto-calibration and validation of SWMM were done for the years 2002–2011 at a daily scale using three EAs: genetic algorithms (GAs), particle swarm optimisation (PSO) and shuffled frog leaping algorithm (SFLA). Statistical parameters like Nash–Sutcliffe effectiveness (NSE), percent bias (PBIAS) and root-mean-squared error–observations standard deviation ratio (RSR) were used to analyse the efficacy of the results. NSE and PBIAS values obtained from GA were superior, with the recorded flow with NSE and PBIAS ranging between 0.63 and 0.69 and between 1.12 and 9.81, respectively, for five discharge locations. The value of RSR was approximately 0 indicating the sensibly exceptional performance of the model. The results obtained from SFLA were robust and superior. Our results showed the prospective use and blending of the hydrodynamic model with EA would aid the decision-makers in analysing the vulnerability in river watersheds. HIGHLIGHTS GA, SFLA and PSO coupled with SWMM to characterise temporal dynamics of river discharge in Upper Damodar River Basin.; GA provided model iteration equivalent to 2,000 and performed robustly with NSE and PBIAS ranging between 0.65 and 0.72 and between 1.51 and 9.51, respectively.; SFLA performed comparatively to GA with a higher convergence speed value, whereas PSO performed satisfactorily.

Exploring Artificial Intelligence Techniques for Groundwater Quality Assessment

Freshwater quality and quantity are some of the fundamental requirements for sustaining human life and civilization. The Water Quality Index is the most extensively used parameter for determining water quality worldwide. However, the traditional approach for the calculation of the WQI is often complex and time consuming since it requires handling large data sets and involves the calculation of several subindices. We investigated the performance of artificial intelligence techniques, including particle swarm optimization (PSO), a naive Bayes classifier (NBC), and a support vector machine (SVM), for predicting the water quality index. We used an SVM and NBC for prediction, in conjunction with PSO for optimization. To validate the obtained results, groundwater water quality parameters and their corresponding water quality indices were found for water collected from the Pindrawan tank area in Chhattisgarh, India. Our results show that PSO–NBC provided a 92.8% prediction accuracy of the WQI indices, whereas the PSO–SVM accuracy was 77.60%. The study’s outcomes further suggest that ensemble machine learning (ML) algorithms can be used to estimate and predict the Water Quality Index with significant accuracy. Thus, the proposed framework can be directly used for the prediction of the WQI using the measured field parameters while saving significant time and effort