Salim Heddam

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

HDR; Full Professor
Teaching , Research Project and Applying Machines Learning Top 2% World Scientists (2020, 2021, 2022)

About

192
Publications
63,719
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
2,661
Citations
Citations since 2017
148 Research Items
2523 Citations
20172018201920202021202220230200400600
20172018201920202021202220230200400600
20172018201920202021202220230200400600
20172018201920202021202220230200400600
Introduction
Salim HEDDAM was born in Skikda, Algeria. He received the Master degree in Agronomy-Hydraulic engineering from Batna University, Algeria, in 1997, the Magister and Ph.D. degrees in Agronomy-Hydraulic engineering from the Higher National Agronomic School, Hydraulic department (ENSA), Algiers, Algeria, in 2006 and 2012, respectively. In 2014, He received the Accreditation to supervise researches (HDR), from Higher National Agronomic School, Hydraulic department. In 2006, he joined the Department of Agronomy, University of 20 Août 1955 Skikda, as an Assistant Professor, in 2014 became an Associate Professor, and in 2019 became a full Professor. His current research interests include Modelling using Artificial Intelligence Technique, Water Resources Analysis Planning and management, Reservoir
Additional affiliations
November 2006 - May 2015
Université 20 août 1955-Skikda
Position
  • Professor (Associate)
November 2006 - February 2020
University 20 Août 1955 SKIKDA 21000
Position
  • Head of Department
Description
  • Full Professor

Publications

Publications (192)
Article
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...
Article
Full-text available
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....
Article
Full-text available
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...
Article
Full-text available
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),...
Chapter
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...
Article
Full-text available
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...
Chapter
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...
Article
Full-text available
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...
Article
Full-text available
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...
Preprint
Full-text available
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...
Article
Full-text available
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),...
Article
Full-text available
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...
Chapter
Full-text available
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...
Chapter
Full-text available
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...
Chapter
Full-text available
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...
Chapter
Full-text available
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...
Chapter
Full-text available
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...
Article
Full-text available
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...
Article
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...
Article
Full-text available
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...
Chapter
Full-text available
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...
Article
Full-text available
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...
Article
Full-text available
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...
Article
Full-text available
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...
Article
Full-text available
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...
Chapter
Full-text available
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...
Chapter
Full-text available
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...
Chapter
Full-text available
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...
Conference Paper
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...
Article
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...
Conference Paper
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...
Conference Paper
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...
Article
Full-text available
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...
Article
Full-text available
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...
Article
Full-text available
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...
Chapter
Full-text available
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...
Chapter
Full-text available
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...
Article
Full-text available
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...
Article
Full-text available
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...
Preprint
Full-text available
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...
Article
Full-text available
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...
Article
Full-text available
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...
Article
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...
Article
Full-text available
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 (...
Conference Paper
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...
Article
Full-text available
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...
Preprint
Full-text available
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),...
Preprint
Full-text available
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...
Article
Full-text available
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...
Chapter
Full-text available
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...
Article
Full-text available
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...
Article
Full-text available
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...
Article
Full-text available
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...
Poster
Full-text available
The 1st International Seminar on pollution, health, environment and biomonitoring 27 * 28 December- Skikda, Algeria
Article
Full-text available
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...
Preprint
Full-text available
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...
Conference Paper
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...
Preprint
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...
Article
Full-text available
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...
Article
Full-text available
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...
Article
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...
Chapter
Full-text available
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...
Article
Full-text available
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...
Article
Full-text available
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...
Chapter
Full-text available
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....
Article
Full-text available
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...
Article
Full-text available
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...
Cover Page
Full-text available
Article
Full-text available
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...
Preprint
Full-text available
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...
Article
Full-text available
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...