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.

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.

Groundwater is being contaminated rapidly due to various anthropogenic activities and geogenic sources. In this direction, assessment of water quality analysis is the basic requirement for nurturing the human being and its evolution. The Water Quality Index (WQI) parameter has been widely used in determining water quality globally. The study aims to provide the suitability of groundwater in the specified region using the polynomial approximation method for drinking and irrigation purposes along with the computation of WQI using the conventional method. Weierstrass's polynomial approximation theorem along with longitudinal and latitudinal values has been used to evaluate the polynomial regarding various physicochemical parameters. To validate the obtained results from the present approach, groundwater quality data collected and analyzed from the Pindrawan tank area in Raipur district, Chhattisgarh, India, have been used. The result is obtained, i.e., the intermediate value of the parameters obtained correctly from the mathematical modeling, with an average error of 7%. This polynomial approximation method can also be used as the substitute of inverse modeling to determine the location of the source in the two-dimensional system. The approach output can be beneficial to administrators in making decisions on groundwater quality and gaining insight into the tradeoff between system benefit and environmental requirement.