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Groundwater in coastal regions is threatened by saltwater intrusion (SWI). Beach nourishment is used in this study to manage SWI in the Biscayne aquifer, Florida, USA, using a 3D SEAWAT model nourishment considering the future sea level rise and freshwater over-pumping. The present study focused on the development and comparative evaluation of seven machine learning (ML) models, i.e., additive regression (AR), support vector machine (SVM), reduced error pruning tree (REPTree), Bagging, random subspace (RSS), random forest (RF), artificial neural network (ANN) to predict the SWI using beach nourishment. The performance of ML models was assessed using statistical indicators such as coefficient of determination (R 2 ), Nash-Sutcliffe efficiency (NSE), means absolute error (MAE), root mean square error (RMSE), and root relative squared error (RRSE) along with the graphical inspection (i.e., Radar and Taylor diagram). The findings indicate that applying SVM, Bagging, RSS, and RF models has great potential in predicting the SWI values with limited data in the study area. The RF model emerged as the best fit and closely matched observed values; it obtained R 2 (0.999), NSE (0.999), MAE (0.324), RRSE (0.209), and RMSE (0.416) during the testing process. The present study concludes that the RF model could be a valuable tool for accurate predictions of SWI and effective water management in coastal areas., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Climate change and excessive greenhouse gas emissions profoundly impact hydrological cycles, particularly in arid and semi-arid regions, necessitating assessments of their effects on water resource management, agriculture, soil fertility, nutrient transport, hydropower generation, and flood risk. This study investigates climate change repercussions on streamflow in the Zarrineh River Basin, Iran, across three decadal intervals (2020-2029, 2055-2064, and 2090-2099) aiming to develop effective adaptation and mitigation strategies. Four General Circulation Models (GCMs), chosen based on distinct Representative Concentration Pathways (RCPs) determined by the annual mean temperature gradient, are employed. These models generate daily maximum (Tmax) and minimum (Tmin) temperatures along with precipitation data. Subsequently, these variables are integrated into the Soil and Water Assessment Tool (SWAT) model to analyze river flow alterations for each decadal timeframe. Comparison between future projections and observed climate data reveals a gradual decline in precipitation and Tmax, coupled with a substantial increase in Tmin. The average precipitation diminishes from 0.77 mm in the period 1985-1994 to a range of 0.42-0.28 mm in 2090-2099. The simulated flow at the basin outlet highlights that the GCM with the highest annual mean temperature gradient yields the lowest streamflow, while conversely, the model with the lowest gradient generates the highest. Consequently, streamflow experiences a decline from 52 m 3 /s in 1985-1994 to a range of 41-20 m 3 /s in 2090-2099., Competing Interests: Declaration of competing interest We declare that there are no conflicts of interest to disclose. We affirm that this research has been conducted with integrity and impartiality, without any personal, financial, or professional relationships that could be perceived as influencing the research or its findings., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

The biochemical oxygen demand (BOD), one of widely utilized variables for water quality assessment, is metric for the ecological division in rivers. Since the traditional approach to predict BOD is time-consuming and inaccurate due to inconstancies in microbial multiplicity, alternative methods have been recommended for more accurate prediction of BOD. This study investigated the capability of a novel deep learning-based model, Deep Echo State Network (Deep ESN), for predicting BOD, based on various water quality variables, at Gongreung and Gyeongan stations, South Korea. The model was compared with the Extreme Learning Machine (ELM) and two ensemble tree models comprising the Gradient Boosting Regression Tree (GBRT) and Random Forests (RF). Diverse water quality variables (i.e., BOD, potential of Hydrogen (pH), electrical conductivity (EC), dissolved oxygen (DO), water temperature (WT), chemical oxygen demand (COD), suspended solids (SS), total nitrogen (T-N), and total phosphorus (T-P)) were utilized for developing the Deep ESN, ELM, GBRT, and RF with five input combinations (i.e., Categories 1-5). These models were evaluated by root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), coefficient of determination (R 2 ), and correlation coefficient (R). Overall evaluations suggested that the Deep ESN5 model provided the most reliable predictions of BOD among all the models at both stations., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier Ltd. All rights reserved.)