Mohammad Zounemat-Kermani

• PhD
• Kerman, Iran at Shahid Bahonar University of Kerman

Solar radiation prediction plays a vital role in many areas of hydrological and water resources planning and management. However, the need for a machine learning (ML) model’s interpretability and explainability has motivated the use of various interpretability methods. For these reasons, the present study was oriented toward the development of robu...

This study investigates the performance of four boosting machine learning models, AdaBoost, XGBoost, CatBoost, and LightGBM, for forecasting maximal (Tmax) and minimal (Tmin) air temperatures at six lead times: the same day and 1, 7, 15, 21, and 30 days ahead. Daily temperature data from the USGS 02187010 weather station (South Carolina, USA) were...

Accurate daily suspended sediment load (SSL) prediction is essential for sustainable water resource management, sediment control, and environmental planning. However, SSL prediction is highly complex due to its nonlinear and dynamic nature, making traditional empirical models inadequate. This study proposes a novel hybrid approach, integrating the...

This study enhances the prediction of biochemical oxygen demand (BOD5), a vital water quality parameter, by developing hybrid artificial neural network models integrated with advanced optimization algorithms. Data from two monitoring stations in South Korea were used to create five models, including the innovative ANN-Enhanced Runge Kutta (ANN-ERUN...

Accurate rainfall-runoff modeling is crucial for effective watershed management, hydraulic infrastructure safety, and flood mitigation. However, predicting rainfall-runoff remains challenging due to the nonlinear interplay between hydro-meteorological and topographical variables. This study introduces a hybrid Gaussian process regression (GPR) mode...

Water quality assessment is critical for ensuring the health of aquatic ecosystems and managing water resources effectively. However, accurately predicting key water quality variables remains challenging due to the complex interactions between environmental factors and anthropogenic influences. In the present investigation, a new modelling framewor...

Groundwater resources constitute one of the primary sources of freshwater in semi-arid and arid climates. Monitoring the groundwater quality is an essential component of environmental management. In this study, a comprehensive comparison was conducted to analyze the performance of nine ensembles and regular machine learning (ML) methods in predicti...

This study investigates the efficacy of hybrid artificial neural network (ANN) methods, incorporating metaheuristic algorithms such as particle swarm optimization (PSO), genetic algorithm (GA), gray wolf optimizer (GWO), Aquila optimizer (AO), Runge-Kutta (RUN), and the novel ANN-based Runge-Kutta with Aquila optimizer (LSTM-RUNAO). The key novelty...

Predicting streamflow is essential for managing water resources, especially in basins and watersheds where snowmelt plays a major role in river discharge. This study evaluates the advanced deep learning models for accurate monthly and peak streamflow forecasting in the Gilgit River Basin. The models utilized were LSTM, BiLSTM, GRU, CNN, and their h...

Meta-heuristic algorithms have been successfully used in solving a variety of engineering problems. The optimal operation of multi-reservoirs is one the most complex engineering problem, so there is an increasing need for the development of such algorithms. In this study, a new hybrid algorithm, Symbiotic Organisms Search based on Moth Swarm Algori...

The provision of drinking water, agricultural, and industrial applications by reservoirs has made lake exploration and monitoring unavoidable. The features of the ecosystem, particularly physical and chemical elements, influence the evaluation of the quality of water resources. Lakes undergo extensive qualitative changes due to their vast amount of...

This study aims to assess the change of drought characteristics (intensity, duration, and frequency) under the effect of climate change in Iran using the modified standardized precipitation index (MSPI) and theory of runs on annual and seasonal scales for three near-future, mid-future (MF), and far-future climates. Hence, regional climate models ex...

Multi-model Ensembles (MMEs) are widely used to reduce uncertainties associated with simulations and projections of GCM/RCM.MMEs combine the results of multiple climate models to produce a more robust and reliable prediction. By considering the range of outputs from different models, MMEs can improve the overall accuracy of climate projections. The...

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 algor...

Sewer networks are not only necessary as an infrastructure for human societies, but they can also help humans achieve a stable situation with the surrounding natural environment by controlling and preventing the spread of pollution in the environment. As a result, concrete sewer maintenance and analysis of their damaging elements are critical. In t...

Accurate prediction of water temperature (T w) will greatly help in improving our understanding of the overall thermal regime fluctuation, and it can help in making sound decisions. While great efforts have been devoted to the development of T w models, further improvement in the prediction accuracy is challenging. Here, we propose a new hybrid mac...

It has been claimed throughout the last two decades that hydrological machine learning (ML) models may produce more accurate and resilient simulations than previous approaches. However, one of the key obstacles to applying these approaches in the field of hydrology is the degradation of estimation error in ML models. The application of integrative...

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 comb...

Different regression-based machine learning techniques, including support vector machine (SVM), random forest (RF), Bagged trees algorithm (BaT), and Boosting trees algorithm (BoT) were adopted for modeling daily reference evapotranspiration (ET0) in a semi-arid region (Hemren catchment basin in Iraq). An assessment of the methods with various inpu...

River flood occurrence disrupts communication and transportation networks, damages buildings and infrastructures, destroys agricultural products and livestock, leading to loss of capital, and endangers human life. Thus, accurate and proper flood prediction and forecasting are major challenges in hydrology and water resources management The present...

This study investigates the feasibility of relevance vector machine tuned with dwarf mongoose optimization algorithm in modeling monthly streamflow. The proposed method is compared with relevance vector machines tuned by particle swarm optimization, whale optimization, marine predators algorithms, and single relevance vector machine methods. Variou...

Biochemical oxygen demand (BOD) is one of the most important parameters used for water quality assessment. Alternative methods are essential for accurately prediction of this parameter because the traditional method in predicting the BOD is time-consuming and it is inaccurate due to inconstancies in microbial multiplicity. In this study, the applic...

Forecasting river flow is an important stage in reservoir operation, urban water management, and water resource optimization. The goal of this research is to forecast daily river flows for two intermittent and ephemeral rivers. Based on the antecedent river flow, the forecasting approach used stochastic (AR, ARIMA, and SARIMA) and machine learning...

The study examines the applicability of six metaheuristic regression techniques—M5 model tree (M5RT), multivariate adaptive regression spline (MARS), principal component regression (PCR), random forest (RF), partial least square regression (PLSR) and Gaussian process regression (GPR)—for predicting short-term significant wave heights from one hour...

Lake water quality plays a vital role in the lake ecosystem, including biotic (for living creatures, such as plants, animals, and micro-organisms) and abiotic interactions. In this research, various types of machine learning (ML) methodologies, such as classification and regression tree (CART), chi-squared automatic interaction detector (CHAID), C5...

Accurate measurements of available water resources play a key role in achieving a sustainable environment of a society. Precise river flow estimation is an essential task for optimal use of hydropower generation, flood forecasting, and best utilization of water resources in river engineering. The current paper presents the development and verificat...

The potential of four different neuro-fuzzy embedded meta-heuristic algorithms, particle swarm optimization, genetic algorithm, harmony search, and teaching–learning-based optimization algorithm, was investigated in this study in estimating the water quality of the Yamuna River in Delhi, India. A cross-validation approach was employed by splitting...

Here, the capability of the Bat algorithm optimized extreme learning machines ELM (Bat-ELM) is demonstrated for river water temperature (Tw) modelling in the Orda River, Poland. Results using the multilayer perceptron neural network (MLPNN), the classification and regression Tree (CART) and the multiple linear regression (MLR) models were presented...

Drought modeling is vital for designing and managing water resource systems due to its significant effects on agriculture and other components of the environment. This study evaluates the prediction accuracy of two newly developed heuristic methods, optimally pruned extreme learning machine (OP-ELM) and dynamic evolving neural-fuzzy inference syste...

Precise estimation of water temperature plays a key role in environmental impact assessment, aquatic ecosystems’ management and water resources planning and management. In the current study, convolutional neural networks (CNN) and long short-term memory (LSTM) network-based deep learning models were examined to estimate daily water temperatures of...

The accurate assessment of groundwater levels is critical to water resource management. With global warming and climate change, its significance has become increasingly evident, particularly in arid and semi-arid areas. This study compares new extreme learning machines (ELM) methods tuned with metaheuristic algorithms such as particle swarm optimiz...

Hybrid metaheuristic algorithm (MA), an advanced tool in the artificial intelligence field, provides precise reference evapotranspiration (ETo) prediction that is highly important for water resource availability and hydrological studies. However, hybrid MAs are quite scarcely used to predict ETo in the existing literature. To this end, the predicti...

This study focuses on the potential of two artificial intelligence techniques in modelling river nitrate concentration using different water quality variables. The proposed models were: (i) the extreme learning machine (ELM) (ii) the deep learning long short-term memory (LSTM), (iii) the Gaussian process regression (GPR) and the support vector regr...

Water quality is an important issue because of its relationship to humans and other living organisms. Predicting water quality parameters is very important for better management of water resources. The decision tree is one of the data mining methods that can create rules for classifying and predicting data using a tree structure. The purpose of thi...

Precise estimation of pan evaporation is necessary to manage available water resources. In this study, the capability of three hybridized models for modeling monthly pan evaporation (Epan) at three stations in the Dongting lake basin, China, were investigated. Each model consisted of an adaptive neuro-fuzzy inference system (ANFIS) integrated with...

For better estimation of renewable environmental friendly and carbon-free energy resources, precise prediction of solar energy is very essential. However, accurate prediction of solar energy is a challenging task due to its fluctuations and due to climatic factors those make it very nonlinear in nature. Therefore, in this study, the novel robust so...

The sedimentation in coastlines, waterways, and reservoirs is an environmental problem that can be coped with sediment removal systems such as hydro-suction. In this study, the Mayfly Algorithm (MA) is applied to predict the maximum depth (hs) and diameter (Ds) of the formed scour hole attributed to the hydro-suction. Two machine learning methods i...

This study introduces a novel meta-heuristic algorithm known as Homonuclear Molecules Optimization (HMO) for optimizing complex and nonlinear problems. HMO is inspired by the arrangement of electrons around atoms given the Bohr atomic model and the structure of homonuclear molecules. This algorithm is based on creating the initial population of a s...

The current research investigates the flow pattern effect on the performance of rectangular fish breeding ponds based on hydraulic efficiency and stagnant regions indicators. Three different flow patterns (linear, vortex cells with rotation in opposite directions, and vortex cells with rotation in one direction) with various inlet flow momentum for...

To achieve better prediction accuracy and robustness, three types of ensemble machine learning such as bagging, boosting, and XGBoost are developed and appraised for the prediction of effluent heavy metals at wastewater treatment plants. Nine potential independent influent parameters were considered for predicting the dissolved concentration of Cr,...

The development of an accurate soft computing method for groundwater level (GWL) forecasting is essential for enhancing the planning and management of water resources. Over the past two decades, significant progress has been made in GWL prediction using machine learning (ML) models. Several review articles have been published, reporting the advanc...

Monthly river flow forecasting has a vital role in many water resource management activities, especially in extreme events such as flood and drought. Therefore, experts need a reliable and precise model for forecasting. The ensemble machine learning (EML) models can provide more accurate results by combining coupled models. To this end, the chief a...

Machines learning models have recently been proposed for predicting rivers water temperature (Tw) using only air temperature (Ta). The proposed models relied on a nonlinear relationship between the Tw and Ta and they have proven to be robust modelling tools. The main motivation for this study was to evaluate how the variational mode decomposition (...

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...

Soil moisture plays an important role in water distribution among various components of hydrological cycle and energy exchanges between the atmosphere and the earth’s surface. Its accurate estimation is necessary for optimal water management in agriculture, environment, and other related fields. This study describes the development and applications...

Climate change is leading to changing patterns of precipitation and increasingly extreme global weather. There is an urgent need to synthesize our current knowledge on climate risks to water security, which in turn is fundamental for achieving sustainable water management. Climate Risk and Sustainable Water Management discusses hydrological extreme...

Empirical relationships between air and water temperatures have been widely described in the literature and a large amount of work has been done on this subject, especially by introducing varieties of approaches ranging from deterministic and energy balance to artificial intelligence models. In the present work, the link between air and water Tempe...

In this study novel integrative machine learning models embedded with the firefly algorithm (FA) were developed and employed to predict energy dissipation on block ramps. The used models include multi-layer perceptron neural network (MLPNN), adaptive neuro-fuzzy inference system (ANFIS), group method of data handling (GMDH), support vector regressi...

This study evaluates the potential of kriging-based (kriging and kriging-logistic) and machine learning models (MARS, GBRT, and ANN) in predicting the effluent arsenic concentration of a wastewater treatment plant. Two distinct input combination scenarios were established, using seven quantitative and qualitative independent influent variables. In...

One of the effective ways to increase the efficiency of weirs is to use nonlinear weirs, such as labyrinth weir, which increases the flow capacity by increasing the length of the weir at a fixed width. Given the importance of precisely estimating the flow discharge coefficient of this type of weir and its impact on supplying the safety of water str...

River sedimentation is an important indicator for ecological and geomorphological assessments of soil erosion within any watershed region. Sediment transport in a river basin is therefore a multi-faceted field yet being a dynamic task in nature. It is characterized by high stochasticity, non-linearity, non-stationarity, and feature redundancy. Vari...

Accurate runoff estimation is crucial for optimal reservoir operation and irrigation purposes. In this study, a novel hybrid method is proposed for monthly runoff prediction in Mangla watershed in northern Pakistan by integrating particle swarm optimization (PSO) and grey wolf optimization (GWO) with extreme learning machine (ELM) as ELM-PSOGWO. Th...

In this study, a new sediment removal system, namely the vortex hydrosuction (VHS) method, is introduced as an alternative hydrosuction technique with higher efficiency. Unlike the conventional hydrosuction systems, VHS benefits from a generated vortex flow for suspending the settled particles. The ability of 27 different VHS systems to remove nonc...

Accurate estimation of the dissolved oxygen concentration is critical and of significant importance for several environmental applications. Over the years, many types of models have been proposed to provide a more accurate estimation of dissolved oxygen at different time scales. Recently, the deep learning paradigm has been increasingly used in sev...

This paper evaluates six soft computational models along with three statistical data-driven models for the prediction of pan evaporation (EP). Accordingly, improved kriging—as a novel statistical model—is proposed for accurate predictions of EP for two meteorological stations in Turkey. In the standard kriging model, the input data nonlinearity eff...

Accurate runoff estimation is crucial for optimal reservoir operation and irrigation purposes. In this study, a novel hybrid method is proposed for monthly runoff prediction in Mangla watershed in northern Pakistan by integrating particle swarm optimization (PSO) and grey wolf optimization (GWO) with extreme learning machine (ELM) as ELM-PSOGWO. Th...

In the current study, an ability of a novel regression-based method is evaluated in modeling daily reference evapotranspiration (ET 0), which is an important issue in water resources management and planning. The method was developed by hybridizing radial basis function and M5 model tree and called as radial basis M5 model tree (RM5Tree). The new mo...

In this study, data-driven methods (DDMs) including different kinds of group method of data handling (GMDH) hybrid models with particle swarm optimization (PSO) and Henry gas solubility optimization (HGSO) methods, and simple equations methods were applied to simulate the maximum hydro-suction dredging depth (hs). Sixty-seven experiments were condu...

Reliable and accurate modeling of groundwater quality is an important element of sustainable groundwater management of productive aquifers. In this research, specific conductance (SC) of groundwater is predicted based on different individual and integrative machine learning, adaptive neuro-fuzzy inference system (ANFIS), and nonlinear mathematical...

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 industria...

The implementation of novel machine learning models can contribute remarkably to simulating the degradation of concrete due to environmental factors. This study considers the sulfuric acid corrosive factor in wastewater systems to simulate concrete mass loss using five machine learning models. The models include three different types of extreme lea...

Soil temperature profile (Ts) is an essential regulator of a plant's root growth, evapotranspiration rates, and hence soil water content. A more accurate model for forecasting Ts presents many worldwide opportunities in improving irrigation efficiency in arid climates and help attain sustainable water resources management. This research compares th...

In this study, two nature-inspired optimization techniques such as firefly algorithm (FA) and butterfly optimization algorithm (BOA) are combined with adaptive neuro-fuzzy inference system (ANFIS) and group method of data handling (GMDH) models for optimal prediction of the complex phenomenon of volumetric concentration of sediment (Cv) in sewer sy...

Recently, there has been a notable tendency towards employing ensemble learning methodologies in assorted areas of engineering, such as hydrology, for simulation and prediction purposes. The diversity of ensemble techniques available for implementation in hydrological sciences has led to the development and utilization of different strategies in th...

We evaluate the potential of kriging-based (kriging and kriging-logistic) and machine learning models (MARS, GBRT, and ANN) in predicting the effluent arsenic concentration of a wastewater treatment plant. Two distinct input combination scenarios were established, using seven quantitative and qualitative independent influent variables. In the first...

This study appraises different types of conventional (e.g., GRNN, RBNN, & MLPNN) and modern neural networks (e.g., integrative, inclusive, hybrid, & recurrent) in forecasting daily flow in the Thames River located in the United Kingdom. The models are mathematically, statistically, and diagnostically compared based on the forecasted results for ten...

In the current study, an ability of a novel regression-based method is evaluated in modelling daily reference evapotranspiration (ET 0 ), which is an important issue in water resources management plans and helps farmers in irrigation planning. The method was developed by hybridizing radial basis function and M5 model tree and called as radial basis...

Sedimentation in dam reservoirs causes problems such as reducing storage volume and useful life of reservoirs, reducing the volume of flood control, sluices, tunnels, and turbines clogged, and other related issues. Despite the development of several methods to solve this problem, the sedimentation rate of the world’s dam reservoirs indicates the ex...

Accurately prediction of streamflow is very important issue for sustainable management of water resources. In this chapter, the applicability of three intelligent data analytic techniques based on the long short-term memory (LSTM) network, extreme learning machines (ELM), and random forest (RF) algorithms is examined in prediction of monthly stream...

This study aims to evaluate the learning ability and performance of five meta-heuristic optimization algorithms in training forward and recurrent fuzzy-based machine learning models, such as ANFIS and RANFIS, to predict hydraulic jump characteristics, i.e., downstream flow depth (h2) and jump length (Lj). To meet this end, the firefly algorithm (FA...

The present study proposes five standard artificial intelligence models including Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), Group Method of Data Handling (GMDH), Multivariate Linear Regression (MLR), and Support Vector Regression (SVR) as well as their integrative models combined with the nature-inspired Firefl...

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, seve...

Drought modeling is vital for designing and managing of water resources systems due to its significant affects on agriculture and other componets of enviorment. This study evaluates the prediction accuracy of two newly developed heuristic methods, optimally-pruned extreme learning machine (OP-ELM) and dynamic evolving neural-fuzzy inference system...

In this research, experimental and numerical modelling of three-phase air, water, and sediment transport flow, due to the opening of a sluice gate was conducted in two scenarios, i.e., with and without a triangular obstacle. Numerical simulation was conducted using the Navier-Stokes equations with the aid of the volume of fluid method (VOF) to trac...

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...

Accurate estimation of dew point temperature (Tdew) has a crucial role in sustainable water resource management. This study investigates kernel extreme learning machine (KELM), boosted regression tree (BRT), radial basis function neural network (RBFNN), multilayer perceptron neural network (MLPNN), and multivariate adaptive regression spline (MARS)...

As a measure of water quality, water turbidity might be a source of water pollution in drinking water resources. Henceforth, having a reliable tool for predicting turbidity values based on common water quantity/quality measured parameters is of great importance. In the present paper, the performance of the online sequential extreme learning machine...

This study evaluates the performance of an integrated version of artificial neural network namely HS-ANN (which is a combination of neural network and heuristic harmony search algorithm) as an alternative approach to predict the sediment transport in terms of sediment volumetric concentration in sewer pipe systems. To overcome the complexities of c...

Neurocomputing methods have contributed significantly to the advancement of modelling techniques in surface water hydrology and hydraulics in the last couple of decades, primarily due to their vast performance advantages and usage amenity. This comprehensive review considers the research progress in the past two decades, the current state-of-the-ar...

The potential of different models – deep echo state network (DeepESN), extreme learning machine (ELM), extra tree (ET) and regression tree (RT) – in estimating dew point temperature by using meteorological variables is investigated. The variables consist of daily records of average air temperature, atmospheric pressure, relative humidity, wind spee...

In this study, the potential of six different machine learning models, gradient boosting tree (GBT), multilayer perceptron neural network (MLPNN), two types of adaptive neuro-fuzzy inference systems (ANFIS) based on fuzzy c-means clustering (ANFIS-FCM) and subtractive clustering (ANFIS-SC), multivariate adaptive regression spline (MARS), and classi...

The present study investigates the capability of two metaheuristic optimization approaches, namely the Butterfly Optimization Algorithm (BOA) and the Genetic Algorithm (GA), integrated with machine learning models in Suspended Sediment Load (SSL) prediction. The Adaptive Neuro-Fuzzy Inference System (ANFIS), Artificial Neural Network (ANN), and Mul...

Due to the high flow velocity over dam spillways and outlets, severe cavitation damage might occur to the structures. Aeration (introducing air into the passing flow) is a useful remedy for preventing or decreasing cavitation, however, proper estimation of aerators air demand is a complex problem. On that account, the standard GMDH model, integrate...

The proper prediction of water sales revenue allows for pricing policies with a specified trend for the optimized use of water resources. The present work focuses on the prediction of the economic status of water sales revenue in a semi-arid environment. To meet this objective, evaporation data (E), dam input water volume (I), and dam output water...