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.

Water is one of the most important natural resources and is widely used around the globe for various purposes. In fact, the agricultural sector consumes 70% of the world’s accessible water, of which about 60% is wasted. Thus, it needs to be managed scientifically and efficiently to maximize food production to meet the requirements of an ever-increasing population. There is a lack of information on water requirements of crops and irrigation scheduling concerning the Shaheed Benazirabad district, Pakistan. Thus, the present study was conducted to determine the irrigation water requirements (IWR) and irrigation scheduling for the major crops in the Shaheed Benazirabad district, Sindh, Pakistan, using agro-climatic data and the CROPWAT model. Agro-climatic data such as rainfall, maximum and minimum temperature, sunshine hours, humidity, and wind speed were obtained from the NASA website, CLIMWAT 2.0, and world weather However, data about studied crops and soils were obtained from FAO (Food and Agriculture Organization). Analysis revealed that the IWRs per irrigation round for the four major crops—sugarcane, banana, cotton, and wheat—were as 3108.0 mm, 1768.5 mm, 1655.7 mm, and 402.5 mm, respectively. It was observed the IWRs are more sensitive in the hot season because of high temperatures and low relative humidity, and vice versa in the cold season. The use of scientific tools such as CROPWAT is recommended to assess IWRs with a high degree of accuracy and to compute irrigation scheduling. Accordingly, the study results will be helpful for improving food production and supervision of water resources.

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.

The many hydrodynamic implications associated with the geomorphological evolution of braided rivers are still not profoundly examined in both experimental and numerical analyses, due to the generation of three-dimensional turbulence structures around sediment bars. In this experimental research, the 3D velocity fields were measured through an acoustic Doppler velocimeter during flume-scale laboratory experimental runs over an emerging sand bar model, to reproduce the hydrodynamic conditions of real braided rivers, and the 3D Turbulent Kinetic Energy (TKE) components were analyzed and discussed here in detail. Given the three-dimensionality of the examined water flow in the proximity of the experimental bar, the statistical analysis of the octagonal bursting events was applied to analyze and discuss the different flume-scale 3D turbulence structures. The main novelty of this study is the proposal of the 3D Hole Size (3DHS) analysis, used for separating the extreme events observed in the experimental runs from the low-intensity events.

Accurate rainfall data with good spatial–temporal distribution remain a challenge worldwide, particularly in arid regions such as western Saudi Arabia, where variability critically influences water resource management and flood mitigation. This study evaluates five satellite-based rainfall products—GPM, GPCP, CHIRPS, PERSIANN-CDR and PERSIANN—against observed monthly rainfall at 28-gauge stations, using the correlation coefficient (CC), root mean square error (RMSE), relative bias (RB) and mean absolute error (MAE). Among uncorrected products, GPM achieved the highest mean CC (0.52), and lowest RMSE (17.0 mm) and MAE (9.18 mm) compared with CC = 0.39 (RMSE 19.9 mm) for GPCP, CC = 0.20 (RMSE 21.6 mm) for CHIRPS, CC = 0.43 (RMSE 19.2 mm) for PERSIANN-CDR and CC = 0.26 (RMSE 57.3 mm) for PERSIANN. Four bias correction methods—linear scaling, nonlinear adjustment, quantile mapping and artificial neural networks (ANN)—were applied. The ANN reduced GPM’s RMSE by 19% to 13.8 mm, increased CC to 0.59, lowered RB to 2.5% and achieved an MAE of 6.89 mm. These results demonstrate that GPM, particularly when bias-corrected via ANN, provides a dependable rainfall dataset for hydrological modeling and flood risk assessment in arid environments.

In the past decade, Saudi Arabia has witnessed a surge in flash floods, resulting in significant losses of lives and property. This raises a need for accurate near-real-time precipitation estimates. Satellite products offer precipitation data with high spatial and temporal resolutions. Among these, the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Dynamic Infrared Rain Rate near-real-time (PDIR-Now) stands out as a novel, global, and long-term resource. In this study, a rigorous comparative analysis was conducted from 2017 to 2022, contrasting PDIR-Now with rain gauge data. This analysis employs six metrics to assess the accuracy of PDIR-Now across various daily rainfall rates and four yearly extreme precipitation indices. The findings reveal that PDIR-Now slightly underestimates light precipitation but significantly underestimates heavy precipitation. Challenges arise in regions characterized by orographic rainfall patterns in the southwestern area of Saudi Arabia, emphasizing the importance of spatial resolution and topographical considerations. While PDIR-Now successfully captures annual maximum 1-day and 5-day precipitation measurements across rain gauge locations, it exhibits limitations in the length of wet and dry spells. This research highlights the potential of PDIR-Now as a valuable tool for precipitation estimation, offering valuable insights for hydrological, climatological, and water resource management studies.