Mohammad Zounemat-Kermani

Shahid Bahonar University of Kerman · Department of Water Engineering

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...

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...

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...

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...

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...

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...

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...

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...

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 resources management. This study investigated 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...

Sediment transportation in water bodies may cause many problems for the water resources projects and damage the environment. Hence, modeling sediment load components, including suspended sediment load (SSL) and bedload (BL) in rivers is of prime importance. Effective modeling of SSL and BL remains a challenging task due to their complex hydrologica...

As water desalination continues to expand globally, desalination plants are continually under pressure to meet the requirements of sustainable development. However, the majority of desalination sustainability research has focused on new desalination projects, with limited research on sustainability performance of existing desalination plants. This...

This research aims to evaluate ensemble learning (bagging, boosting, and modified bagging) potential in predicting microbially induced concrete corrosion in sewer systems from the data mining (DM) perspective. Particular focus is laid on ensemble techniques for network-based DM methods, including multi-layer perceptron neural network (MLPNN) and ra...

Proper estimation of the critical flow velocity of slurries (Vc) is one of the most important parameters to design slurry transport in pipeline systems. In this study, three standard soft computing data-driven models including artificial neural network (ANN), group method of data handling (GMDH), and neuro-fuzzy inference system (ANFIS) as well as...

Bridge failure, due to local scour at bridge pier foundations, has become a critical issue in river and bridge engineering, which might lead to transportation disruption, loss of lives and economic problems. A practical solution to prevent bridge collapses is the implementation of scour mitigation methods around bridge foundations. Based on an expe...

This study challenges the use of three nature‐inspired algorithms as learning frameworks of the adaptive‐neuro‐fuzzy inference system (ANFIS) machine learning model for short‐term modeling of dissolved oxygen (DO) concentrations. Particle swarm optimization (PSO), butterfly optimization algorithm (BOA), and biogeography‐based optimization are emplo...

The highest cost of forecasting for a water distribution network is compared to the design and oversight section of the project. If the goal is to reduce costs, part of this can be achieved by reducing the cost of pipes used in the network and the standard pressure of water in the nodes. This study is a multiobjective optimization function that wil...

The accuracy of four evolutionary neuro fuzzy methods, adaptive neuro-fuzzy inference system with particle swarm optimization (ANFIS-PSO), ANFIS with genetic algorithm (ANFIS-GA), ANFIS with ant colony algorithm (ANFIS-ACO) and ANFIS with butterfly optimization algorithm (ANFIS-BOA), is investigated and compared with classical ANFIS method in forec...

This paper reports the effect of straight furrow (SF) and meandering furrow (MF) irrigation strategies, as well as inflow rate, on infiltration and hydraulic parameters including advance time, recession time, and runoff hydrograph. The performance of SF and MF irrigation in terms of runoff ratio, deep percolation, and application efficiency was eva...

Estimation of suspended sediments carried by natural rivers is essential for projects related to water resource planning and management. This study proposes a dynamic evolving neural fuzzy inference system (DENFIS) as an alternative tool to estimate the suspended sediment load based on previous values of streamflow and sediment. Several input scena...