Salim Heddam

University 20 Août 1955 SKIKDA · Faculty of Science, Agronomy Department, Hydraulic Division

Accurate daily solar radiation prediction is a crucial task for the management and generation of solar energy as one of the alternatives to fossil fuels. In this study, the prediction accuracy of new machine learning methods, wavelet long short-term memory (WLSTM), wavelet multi-layer perceptron artificial neural network (WMLPANN), long short-term...

Information about water resources is crucial for sustainable development, and this issue is considered to be one of the most important concerns worldwide due to rapid industrialization and population growth. Countries in the semiarid region of the western Asia, like Iran, are dependent on groundwater resources so access to these resources is vital....

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...

In the present paper, we propose a new approach for monthly streamflow prediction based on the extreme learning machine (ELM) and the metaheuristics Bat algorithm (BAT-ELM). The performances of the BAT-ELM were compared to those of ELM, support vector regression (SVR), Gaussian process regression (GPR), multilayer perceptron neural network (MLPNN),...

During the last few years, monitoring and controlling water quality in freshwater ecosystems was strongly facilitated by the increasing number of in situ stations, certainly in combination with the high number of developed models. Several water quality variables have received a great deal of attention regarding their environmental importance, while...

Porosity is a key variable for hydrocarbon reservoirs evaluation. It can be directly determined in laboratory tests using core samples or calculated indirectly from well logs. However, these methods are expensive and time consuming affecting the cost of supply of oil produced. Monitoring drilling variables in real time as a borehole is drilled can...

Streamflow forecasting using advance machine learning models have received great importance during the last few years regarding its importance for water resources management, especially for facing climate change. Several approaches based on the exploitation of a wide variety of models have been proposed and successfully applied for accurately daily...

In the present study, three machine learning methods were applied for predicting seepage flow through embankment dams, namely (i) support vector regression (SVR), relevance vector machine (RVM), and Gaussian process regression (GPR). The three models were developed using seepage flow (Q: L/mn) and piezometer level (Z:m) measured at several piezomet...

Water engineering problems are typically nonlinear, multivariable, and multimodal optimization problems. Accurate water engineering problem optimization helps predict these systems' performance. This paper proposes a novel optimization algorithm named enhanced multioperator Runge-Kutta optimization (EMRUN) to accurately solve different types of wat...

Hybrid model selection built with models based on machine learning (ML) and Deep learning (DL) has a significant impact on river flow predictions. Sustainable use of water resources is possible with the evaluation of basin management principles, effective natural resource management and correct water resources planning. These conditions require acc...

In order to evaluate and project the quality of groundwater utilized for irrigation in the Sahara aquifer in Algeria, this research employed irrigation water quality indices (IWQIs), artificial neural network (ANN) models, and Gradient Boosting Regression (GBR), alongside multivariate statistical analysis and a geographic information system (GIS),...

Accurate streamflow estimation is crucial for proper water management for irrigation, hydropower, drinking and industrial purposes. The main aim of this study to adopt new data preprocessing techniques (e.g., EMD, EEMD and EWT) to capture the data noise and to enhance the prediction accuracy of machine learning methods for streamflow estimation whi...

Modelling river water temperature using air temperature is broadly discussed in the literature, and up to now, all proposed models were based on establishing a direct relationship between water and air temperature variables at different time scale. The need for a stronger link between these two variables was strongly emphasized, and it was demonstr...

Turbidity (TU) is one of the most important water quality variables and despite its great importance, the need to increase the number of monitoring stations is becoming a major issue for many regions of the world. In the absence of direct in situ measurement, alternative methods based on the different modelling approaches can be useful tools for pr...

Understanding the relationship between soil temperature (Ts) and air temperature (Ta) is become of great importance and, a great deal of research is undertaken to demonstrate the strong correlation between the two variables. In the major part of the studies conducted previously, the Ts was linked to the Ta via a large amount of variables in the pre...

Supersaturation of total dissolved gas (TDG) in water has become a serious problem to which a great deal of attention has been devoted during the last few decades. High level of TDG can cause gas bubble trauma (GBT) and it may be caused by releasing water through the spillways of dams. In situ monitoring of total dissolved gas can help in understan...

Chlorophyll-a (Chl-a) concentration is the most used water variable for the quantification of water eutrophication. High level of Chlorophyll-a can cause the degradation of the fresh water, accelerate the production of taste and odor, and have the greatest potential for adverse ecological and human health effects. While a number of existing standar...

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...

The computer aided models have received much attention in the recent years for solving diverse civil engineering applications. In the current review, the applications of artificial intelligence (AI) methodologies in modeling beam shear strength are presented. The review is attempted to give an insightful version for AI models progression in modelin...

Urban areas are quickly established, and the overwhelming population pressure is triggering heat stress in the metropolitan cities. Climate change impact is the key aspect for maintaining the urban areas and building proper urban planning because spreading of the urban area destroyed the vegetated land and increased heat variation. Remote sensing–b...

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...

Lakes help increase the sustainability of the natural environment and decrease food chain risk, agriculture, ecosystem services, and leisure recreational activities locally and globally. Reliable simulation of monthly lake water levels is still an ongoing demand for multiple environmental and hydro-informatics engineering applications. The current...

Mixed data is a combination of continuous and categorical variables and occurs frequently in fields such as agriculture, remote sensing, biology, medical science, marketing, etc., but only limited work has been done with this type of data. In this study, data on continuous and categorical characters of 452 genotypes of cotton (Gossypium hirsutum) w...

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...

Mixed data is a combination of continuous and categorical variables and occurs frequently in fields such as agriculture, remote sensing, biology, medical science, marketing, etc., but only limited work has been done with this type of data. In this study, data on continuous and categorical characters of 452 genotypes of cotton (Gossypium hirsutum) w...

This study uses the empirical wavelet transform (EWT) for improving the estimation of soil moisture. We used the bidirectional long short-term memory (BiLSTM), the support vector regression (SVR) and the Gaussian process regression (GPR) for modelling soil moisture using only soil temperature. The performances of the models were evaluated using RMS...

In the present chapter, we use the empirical mode decomposition (EMD), the ensemble EMD (EEMD), and the complete ensemble EMD with adaptive noise (CEEMDAN) for dissolved oxygen (DO) prediction. First, based on water temperature (Tw), DO was modeled using three machines learning models, namely, extreme learning machine (ELM), the ELM optimized Bat a...

In the present chapter, we propose a new modelling framework for predicting river turbidity using only river discharge as predictor using three models, i.e., random vector functional link neural network (RVFL), generalized regression neural network (GRNN), and the radial basis function neural network (RBFNN). First, the models were applied using on...

Streamflow is one of the main components of the hydrological cycle which must be carefully studied. Better Streamflow forecasting is of great importance for water resources planning and management, as well as early warning and mitigation of natural disasters such as droughts and floods. In this paper, a comparative study of three models based on ma...

Evapotranspiration is a non-linear and complex phenomenon requiring different climatic variables for accurate estimation. In this study, the performance of several artificial intelligence models in estimating the amount of monthly reference evapotranspiration was investigated. Babolsar and Ramsa regions located in the north of Iran were selected as...

High nonlinearity and nonstationarity of streamflow have been instrumental in the development of robust forecasting models. In this paper, a comparative study of two models based on machine-learning approaches for daily streamflow forecasting was done using data of rainfall and runoff collected at Oued Cheliff Harrezal catchment located in Cheliff...

Streamflow forecasting may provide strategic information and can help in better improving water resources management systems. In this paper, A comparative study of two different models based on machine-learning approaches for daily streamflow forecasting was done using data of rainfall and runoff collected at Oued Ras Ouahrane River catchment locat...

Biochar is a carbon-based substance made by the pyrolysis of organic waste. The amount of biochar produced is determined by the type of feedstock and pyrolysis conditions. Biochar is frequently added to the soil for various reasons, including carbon sequestration, greenhouse gas mitigation, improved crop production by boosting soil fertility, remov...

Biochar is a carbon-based substance made by the pyrolysis of organic waste. The amount of biochar produced is determined by the type of feedstock and pyrolysis conditions. Biochar is frequently added to the soil for various reasons, including carbon sequestration, greenhouse gas mitigation, improved crop production by boosting soil fertility, remov...

As an indicator measured by incubating organic material from water samples in rivers, the most typical characteristic of water quality items is biochemical oxygen demand (BOD5) concentration, which is a stream pollutant with an extreme circumstance of organic loading and controlling aquatic behavior in the eco-environment. Leading monitoring approa...

River water temperature (Tw) is highly dynamic in space and time and can vary over the seasons and between day and night, and can affect the health condition of the aquatic organisms in multiple ways. Remarkably, modelling water Tw has received great attention during the last few years, especially, using machine learning models, however, few studie...

In the present work, a novel hybrid model based on signal processing decomposition, extreme learning machine and parallel chaos search were proposed for forecasting DO several days in advance. First, the correlation between DO data at several times lags were calculated using the autocorrelation function (ACF) and the partial autocorrelation functio...

Nowadays, great attention has been attributed to the study of runoff and its fluctuation over space and time. There is a crucial need for a good soil and water management system to overcome the challenges of water scarcity and other natural adverse events like floods and landslides, among others. Rainfall-runoff (R-R) modeling is an appropriate app...

Dams significantly impact river hydrology by changing the timing, size, and frequency of low and high flows, resulting in a hydrologic regime that differs significantly from the natural flow regime before the impoundment. For precise planning and judicious use of available water resources for agricultural operations and aquatic habitats, it is crit...

Nowadays, great attention has been attributed to the study of runoff and its fluctuation over space and time. There is a crucial need for a good soil and water management system to overcome the challenges of water scarcity and other natural adverse events like floods and landslides, among others. Rainfall-runoff modeling is an appropriate approach...

This study aims to evaluate the usefulness and effectiveness of four machine learning (ML) models for modelling cyanobacteria blue-green algae (CBGA) at two rivers located in the USA. The proposed modelling framework was based on establishing a link between five water quality variables and the concentration of CBGA. For this purpose, artificial neu...

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...

Direct soil temperature (ST) measurement is time-consuming and costly; thus, the use of a simple and cost-effective machine learning (ML) tool is helpful. In this study, ML approaches, including KStar, instance-based K-nearest learner (IBK) and locally weighted learner (LWL) coupled with resampling algorithms of bagging (BA) and dagging (DA) were d...

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 (...

Streamflow forecasting may provide strategic information and can help in better improving water resources management systems. In this paper, a comparative study of three models based on machine-learning approaches for daily streamflow forecasting was done using data of rainfall and runoff collected at Arib Cheliff River catchment located in Cheliff...

In this study, the viability of radial M5 model tree (RM5Tree) is investigated in prediction and estimation of daily streamflow in a cold climate. The RM5Tree model is compared with the M5 model tree (M5Tree), artificial neural networks (ANN), radial basis function neural networks (RBFNN), and multivariate adaptive regression spline (MARS) using da...

Pan evaporation modelling and forecasting is needed to provide timely, continuously, and valuable water information to support water management. In this study we predict weekly pan evaporation using six machine learning approaches.The proposed models were:the multiple linear regression (MLR), the multiple nonlinear least-squares regression (MNLSR),...

Dams significantly impact river hydrology, mainly by changing the timing, size, and frequency of low and high flows, resulting in a hydrologic regime that differs significantly from the natural flow regime before the impoundment. For precise planning and judicious use of available water resources for agricultural operations and aquatic habitats, it...

This study surveys two rule-based regression models for index rainfall (IR) estimation. The so-called IR is frequently considered a fundamental variable in the rainfall-runoff modeling. It is also required in regional frequency analysis (RFA). For this aspect, no case study has been carried out so far in the Algerian context. The data used in this...

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...

Dissolved oxygen (DO) concentration is an important water-quality parameter, and its estimation is very important for aquatic ecosystems, drinking water resources, and agro-industrial activities. In the presented study, a new support vector machine (SVM) method, which is improved by hybrid firefly algorithm–particle swarm optimization (FFAPSO), is...

A novel inverse intelligent model is developed to predict porosity in real time while drilling. It applies established machine learning (ML) models to gas-while-drilling (GWD) data calibrated with core porosity measurements for a saturated oil reservoir. The data are associated with the Cambro-Ordovician sandstone reservoir in the giant Hassi Messa...

Direct soil temperature (ST) measurement is time-consuming and costly; thus, the use of a simple and cost-effective machine learning (ML) tool is helpful. In this study, ML approaches, including KStar, instance-based K-nearest learner (IBK) and locally weighted learner (LWL) coupled with resampling algorithms of bagging (BA) and dagging (DA) were d...

https://sipseb.org/2021/papersubmission/

The 1st International Seminar on pollution, health, environment and biomonitoring 27 * 28 December- Skikda, Algeria

Hybrid heuristic algorithm (HA), an innovative technique in the machine learning field, enhances the accuracy of reference evapotranspiration (ETo) prediction, which is of paramount significance for regional water management, agricultural planning, and irrigation designing. However, the new hybrid HA techniques, namely Moth-Flame Optimization Algor...

Moth-Flame Optimization Algorithm (MFO) and Water Cycle Optimization Algorithm (WCA) are rarely applied to estimate ETo in the earlier literature. Therefore, this study assessed prediction and the estimation abilities of a novel hybrid adaptive neuro-fuzzy inference system (ANFIS-WCAMFO) for monthly ETo of Dhaka and Mymensing stations with data-lim...

Streamflow is one of the main components of the hydrological cycle which must be carefully studied. Better Streamflow forecasting is of great importance for water resources planning and management, as well as early warning and mitigation of natural disasters such as droughts and floods. In this paper two artificial intelligence techniques models we...

The capability of a new machine learning model, fuzzy c-means based neuro-fuzzy system calibrated with hybrid particle swarm optimization-gravitational search algorithm (ANFIS-FCM-PSOGSA) in improving the prediction accuracy of river suspended sediment loads (SSL) is investigated in this study. The outcomes of the proposed method were compared with...

Reliable modeling of river sediments transport is important as it is a defining factor of the economic viability of dams, the durability of hydroelectric-equipment, river susceptibility to pollution, suitability for navigation, and potential for aesthetics and fish habitat. The capability of a new machine learning model, fuzzy c-means based neuro-f...

Estimation of solar radiation (SR) carries importance for planning available renewable energy, and it is also beneficial for solving agricultural, meteorological , and engineering problems. This study compares the ability of hybrid adaptive neuro fuzzy (ANFIS) models and long short-term memory to search a suitable approach for SR prediction with mi...

Determination of wetting patterns' dimensions is essential in designing and managing surface/subsurface drip irrigation systems. The laboratory experiments were conducted using physical model with dimensions of 3 × 1 × 0.5 m 3 to evaluate the moisture redistribution process under continuous and pulse surface/subsurface irrigation systems. In the pr...

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...

One of the main concerns of environmental and ecological managers for rivers, lakes, reservoirs and marine ecosystems is developing a reliable and efficient predictive model for chlorophyll-a concentration. In this study, online sequential extreme learning machine, M5Prime tree, multi-layer perceptron artificial neural network, response surface met...

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...

Streamflow plays an important role in several hydraulic and hydrologic applications. Therefore, forecasting streamflow accurately is critical for better understanding of streamflow characteristics and variability over the time. This chapter proposed and discussed four heuristic extreme learning machine (ELM) models used for forecasting steamflows....

Accurate estimation of suspended sediment (SS) is very essential for planning and management of hydraulic structures. The study investigates the accuracy of four machine learning methods, dynamic evolving neural-fuzzy inference systems (DENFIS), fuzzy cmeans based adaptive neuro fuzzy system (ANFIS-FCM), multivariate adaptive regression spline (MAR...

Study region Sixteen different sites from two provinces (Lorestan and Illam) in the western part of Iran were considered for the field data measurement of cumulative infiltration, infiltration rate, and other effective variables that affect infiltration process. Study focus Soil infiltration is recognized as a fundamental process of the hydrologic...

The accurate estimation of suspended sediments (SSs) carries significance in determining the volume of dam storage, river carrying capacity, pollution susceptibility, soil erosion potential, aquatic ecological impacts, and the design and operation of hydraulic structures. The presented study proposes a new method for accurately estimating daily SSs...

The presented study proposes a new method for accurately estimating suspended sediments us-ing antecedent discharge and sediment information. The method is developed by hybridizing multivariate adaptive regression spline (MARS) and Kmeans clustering algorithm (MARS-KM). The efficacy of the proposed method is established by comparing its performance...

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...