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The aim of the present paper is the study of interaction of the abrupt wave with vertical and inclined rectangular obstacles. For this purpose, in the first step, two experiments have been done. The tests were performed with smooth rectangular cross-section channels, and related data were extracted using digital image processing. Flow behavior was recorded with one adjacent CCD camera through the glass walls of the entire downstream channel. In the second step, the numerical study has been done by a mesh-free particle Lagrangian method (Incompressible Smoothed Particle Hydrodynamics, ISPH) and a mesh-based Eulerian method (Finite Volume Method with Volume of Fluid surface tracking approach, FV-VOF). The capabilities of the numerical methods in simulation of the sudden variations free surface flows have been assessed. A comparison between the computed results and the experimental data shows that both numerical models simulate the mentioned flows with reasonable accuracy. [ABSTRACT FROM AUTHOR]

In the present study the effect of the skew-angle of the alignment of tandem piers on local scour depth around them is investigated. The tandem piers were aligned with different skew-angles of θ=0°,30°,45°,60°,90° with respect to the flow direction. The results indicatethat with the increment of the skew-angle, the influence of sheltering effects is decreased. In other word, since the sheltering effect of the upstream pier is declined (which reduces the approach velocity for the downstream pier) the scour depth around downstream pier increases. The results show that the maximum scour depth occurs at both piers for the skew-angle of θ=45°.Furthermore, the best configuration to aligned tandem piers was achieved at the skew-angle of θ=30°.

A hybrid simulation-optimization model is proposed for the optimal conjunctive operation of surface and groundwater resources. This second-level model is created by finding and combining the best aspects of two resilient metaheuristics, the moth swarm algorithm and the symbiotic organization search algorithm, and then connecting the resulting algorithm to an artificial neural network simulator. For assessment of the developed model efficiency, its results are compared with two first-level simulation-optimization models. The comparisons reveal that the operation policies obtained by the developed second-level model can reliably supply more than 99% of the total demands in the study regions, indicating its superior efficiency compared to the two other first-level models. In addition, the highest sustainability index in the study regions belongs to the proposed model. Comparing the results of this research with those of other recent studies confirm the supremacy of the developed second-level model over several previously developed models., (© 2024. The Author(s).)

Wastewater quality modelling plays a vital role in planning and management of wastewater treatment plants ( WWTP ). This paper develops a new hybrid machine learning model based on extreme learning machine ( ELM ) optimized by Bat algorithm ( ELM-Bat ) for modelling five day effluent biochemical oxygen demand ( BOD 5 ). Specifically, this hybrid model combines the Bat algorithm for model parameters optimization and the standalone ELM. The proposed model was developed using historical measured effluents wastewater quality variables, i.e., the chemical oxygen demand ( COD ), temperature, pH, total suspended solid ( TSS ), specific conductance ( SC ) and the wastewater flow ( Q ). The performances of the hybrid ELM-Bat were compared with those of the multilayer perceptron neural network ( MLPNN ), the random forest regression ( RFR ), the Gaussian process regression (GPR), the random vector functional link network (RVFL), and the multiple linear regression (MLR) models. By comparing several input variables combination, the improvement achieved in the accuracy of prediction through the hybrid ELM-Bat was quantified. All models were first calibrated using training dataset and later tested using validation and based on four performances metrics namely, root mean square error (RMSE), mean absolute error (MAE), the correlation coefficient (R), and the Nash-Sutcliffe model efficiency (NSE). In all, it is concluded that the ELM-Bat is the most accurate model when all the six input were included as input variables, and it outperforms all other benchmark models in terms of predictive accuracy, exhibiting RMSE, MAE, R and NSE values of approximately, 0.885, 0.781, 2.621, and 1.989, respectively., Competing Interests: 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., (© 2023 The Authors.)

Wave-induced inundation in coastal zones is a serious problem for residents. Accurate prediction of wave run-up height is a complex phenomenon in coastal engineering. In this study, several machine learning (ML) models are developed to simulate wave run-up height. The developed methods are based on optimization techniques employing the group method of data handling (GMDH). The invasive weed optimization (IWO), firefly algorithm (FA), teaching-learning-based optimization (TLBO), harmony search (HS), and differential evolution (DE) meta-heuristic optimization algorithms are embedded with the GMDH to yield better feasible optimization. Preliminary results indicate that the developed ML models are robust tools for modeling the wave run-up height. All ML models' accuracies are higher than empirical relations. The obtained results show that employing heuristic methods enhances the accuracy of the standard GMDH model. As such, the FA, IWO, DE, TLBO, and HS improve the RMSE criterion of the standard GMDH by the rate of 47.5%, 44.7%, 24.1%, 41.1%, and 34.3%, respectively. The GMDH-FA and GMDH-IWO are recommended for applications in coastal engineering., (© 2022. The Author(s).)

Groundwater is one of the most important freshwater resources, especially in arid and semi-arid regions where the annual amounts of precipitation are small with frequent drought durations. Information on qualitative parameters of these valuable resources is very crucial as it might affect its applicability from agricultural, drinking, and industrial aspects. Although geo-statistics methods can provide insight about spatial distribution of quality factors, applications of advanced artificial intelligence (AI) models can contribute to produce more accurate results as robust alternative for such a complex geo-science problem. The present research investigates the capacity of several types of AI models for modeling four key water quality variables namely electrical conductivity (EC), sodium adsorption ratio (SAR), total dissolved solid (TDS) and Sulfate (SO4) using dataset obtained from 90 wells in Tabriz Plain, Iran; assessed by k-fold testing. Two different modeling scenarios were established to make simulations using other quality parameters and the geographical information. The obtained results confirmed the capabilities of the AI models for modeling the well groundwater quality variables. Among all the applied AI models, the developed hybrid support vector machine-firefly algorithm (SVM-FFA) model achieved the best predictability performance for both investigated scenarios. The introduced computer aid methodology provided a reliable technology for groundwater monitoring and assessment., Competing Interests: The authors have declared that no competing interests exist.

Climate variability is heavily impacting human health all around the globe, in particular, on residents of developing countries. Impacts on surface water and groundwater resources and water-related illnesses are increasing, especially under changing climate scenarios such as diversity in rainfall patterns, increasing temperature, flash floods, severe droughts, heatwaves and heavy precipitation. Emerging water-related diseases such as dengue fever and chikungunya are reappearing and impacting on the life of the deprived; as such, the provision of safe water and health care is in great demand in developing countries to combat the spread of infectious diseases. Government, academia and private water bodies are conducting water quality surveys and providing health care facilities, but there is still a need to improve the present strategies concerning water treatment and management, as well as governance. In this review paper, climate change pattern and risks associated with water-related diseases in developing countries, with particular focus on Pakistan, and novel methods for controlling both waterborne and water-related diseases are discussed. This study is important for public health care, particularly in developing countries, for policy makers, and researchers working in the area of climate change, water quality and risk assessment., Competing Interests: The authors declare no conflict of interest.

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.)

Chlorination, the basic treatment utilized for drinking water sources, is widely used for water disinfection and pathogen elimination in water distribution networks. Thereafter, the proper prediction of chlorine consumption is of great importance in water distribution network performance. In this respect, data mining techniques-which have the ability to discover the relationship between dependent variable(s) and independent variables-can be considered as alternative approaches in comparison to conventional methods (e.g., numerical methods). This study examines the applicability of three key methods, based on the data mining approach, for predicting chlorine levels in four water distribution networks. ANNs (artificial neural networks, including the multi-layer perceptron neural network, MLPNN, and radial basis function neural network, RBFNN), SVM (support vector machine), and CART (classification and regression tree) methods were used to estimate the concentration of residual chlorine in distribution networks for three villages in Kerman Province, Iran. Produced water (flow), chlorine consumption, and residual chlorine were collected daily for 3 years. An assessment of the studied models using several statistical criteria (NSC, RMSE, R 2 , and SEP) indicated that, in general, MLPNN has the greatest capability for predicting chlorine levels followed by CART, SVM, and RBF-ANN. Weaker performance of the data-driven methods in the water distribution networks, in some cases, could be attributed to improper chlorination management rather than the methods' capability.