The study utilizes a dataset with seven critical constraints and creates models that are estimated based on various metrics. The goal is to categorize and properly predict the water quality index (WQI) using the suggested models. The outcomes show that the implied models can accurately assess water quality and forecast WQI with high rates of success. Temperature, pH, dissolved oxygen (DO), conductivity, total dissolved solids (TDS), turbidity, and chlorides (Cl-) are some of the six crucial factors used in the study’s dataset. The mean absolute error (MAE), mean squared error (MSE), and coefficient of determination (R²) are some of the metrics used to develop and assess the Artificial Neural Networks (ANN) and Long Short-Term Memory (LSTM) models. The study also makes use of heat maps and correlation graphs to shed further light on the connections between various water quality measures. The color-coded values of the seven parameters, which represent the water quality level of the sample, are displayed on the heat map. The link between the two parameters is shown by the correlation graph between TDS and turbidity, which depicts their correlation coefficient. The study’s results show how effective machine learning algorithms may be as a tool for observing surface water quality. Himachal Pradesh is the tourist hub, so with the rapid increase in the volume of surface water contamination, the application of artificial intelligence will give a better view of data analytics and help with prediction and modeling. It was obtained from the study that the mean square error and root mean square error of ANN and LSTM lie between 0.52–6.0 and 0.04–0.21, respectively. However, the LSTM model’s accuracy is 95%, which is higher than the ANN model. The study highlights the importance of leveraging machine learning techniques in water quality monitoring to ensure the protection and management of water resources. With advancements in machine learning, artificial intelligence (AI) techniques have emerged as a promising tool for surface water quality monitoring. The major goal of the study is to explore the potential of two types of machine learning algorithms, namely artificial neural networks (ANNs) and long short-term memory (LSTM) models, for surface water quality monitoring.

The water quality of rivers is deteriorating due to human interference. It is essential to understand the relationship between human activities and land use types to assess the water quality of a region. GIS is the latest tool for analyzing this spatial correlation. Land use land cover, and change detection are the best illustration for showing the human interactions with land features. This study assessed water quality index of the upper Ganges River near Haridwar, Uttarakhand, and spatially correlated it with changing land use to reach a logical conclusion. In the upper course of Ganges, along a 78-km stretch from Kaudiyala to Bhogpur, water samples were collected from five stations. For water quality index, physicochemical parameters like pH, EC, DO, TDS, CaCO₃⁻, CaCO₃, Cl⁻, Ca²⁺, Mg²⁺, Na⁺, K⁺, F⁻, Fe²⁺ were considered. The results of the spatial analysis were evaluated through error estimation and spatial correlation. The root mean square error between spatial land use and water quality index at the selected sampling sites was estimated to be 0.1443. The spatial correlation between land use change and site-wise differences in water quality index also showed a high positive correlation, with R² = 0.8455. The degree of positive correlation and root mean square error strongly indicated that the water quality of the river in the upper course of the Ganges is highly impacted by human activities.

In the present study, the quality of the surface water of the Gomti river (Lucknow, India) was investigated. Lucknow is situated in the centre of Uttar Pradesh, which is most the populated state in India. The locality has experienced rapid, unregulated development activities and population growth in recent decades, both of which have had a negative impact on its ecosystem and environment. Continuous monitoring is required to maintain the ecosystem at the desired level. Nine samples of river water were collected from the Gomti River in Lucknow, and they were analysed for a total of nine different characteristics, including pH, turbidity (Tur), dissolved oxygen (DO), total dissolved solids (TDSs), chemical oxygen demand (COD), chloride ion (Cl-) concentration, temperature (T), biochemical oxygen demand (BOD₅) and total hardness (TH). The observed data were analysed using multivariate statistical methods. A cluster analysis (CA) was used to sort the sampling locations into different groups, and a principal component analysis (PCA) was used to find the different sources of pollution. Using a cluster analysis, all the water quality parameters were divided into three groups. Cluster 1 represented the less polluted sites, cluster 2 represented the moderately polluted sites and cluster 3 represented the highly polluted sites. Sampling sites SS8, SS4, S99 and SS7 were highly polluted because of nearby pollution sources such as domestic wastewater and runoff storm water. The principal component analysis yielded two meaningful components that explained 82.4% of the total variation in the data. The first factor and second factor explained 59.022 and 23.363 percentages of the total variance, respectively. It was noticed that major sources of pollution for the Gomti river are storm water runoff and the release of domestic and industrial wastewater from residents and industries, respectively. This study will help policy makers to ensure sustainable practices and reduce negative impacts on the availability and quality of water, allowing for the most efficient use of the Gomti River.

Soil is a heterogeneous material, and its properties are vital from an agricultural perspective and for groundwater management. However, limited studies have been performed on the soil characteristics (soil texture, water-holding capacity, and soil compositions) of a single pumping well, especially in Malaysia. This article focuses on the soil characteristics and elemental analysis of a single borehole with 11 samples collected around Labu Kubong, Perak. The soil properties were analyzed in the context of particle size distribution (PSD) using the laser diffraction method (LDM), as well as soil composition for elemental analysis using a scanning electron microscope coupled with an energy dispersive X-ray (SEM-EDX). The LDM results revealed the average percentage of clay, silt, and sand to be 0%, 6%, and 94%, respectively, indicating most particles comprised sand particles which in percentages demonstrated a sandy texture with less silt content. Additionally, the water holding capacity is low because of major coarse sand particles in alluvial formations. Moreover, SEM-EDX outcomes displayed an average percentage of elemental composition reported as follows: C (40.77%), O (34.33%), Si (10.66%), Al (5.82%), Fe (1.10%), K (1.10%), As (0.05%), Na (0.04%), and Be (5.62%). Consequently, SEM-EDX outcomes showed these elements were derived from silicified quartz, feldspar, and iron-bearing minerals that originated from shale formations, and the presence of carbon indicates peat formation. Therefore, this study provides information on a single pumping well from an irrigation practice, and this study also recommends regional to global scale studies for supporting sustainable groundwater development worldwide.