Salim Heddam

• HDR; Full Professor
• Full Professor at University 20 Août 1955 SKIKDA

Drought is India's foremost concern because of global warming, low precipitation, deforestation, and temperature variation. Madhya Pradesh is the central state of India, where drought risks are gradually recorded as high vulnerability due to low rainfall and water shortage. Drought monitoring indices like the standardized precipitation evapotranspi...

The supersaturation of total dissolved gas (TDG) in rivers serves as a critical indicator of water quality downstream of high dams. This study models TDG levels at two monitoring stations in the Columbia and Snake River Basins (USA), where high TDG concentrations were recorded. Hourly data on water temperature, barometric pressure, dam spill, senso...

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...

This study evaluates the effectiveness of hyperspectral data to retrieve chlorophyll a (Chl-a) concentrations using various Machine Learning (ML) methods, specifically to determine whether spectral reflectance can provide accurate estimations of Chl-a. The study aims to address the gap in understanding how hyperspectral measurements correlate with...

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...

In this work, the potential use of native β-cyclodextrin (β-CD) as an encapsulating agent for dihydroquercetin (DHQ) was evaluated. Based on the experimental results, which showed the 1:1 inclusion stoichiometry, a computational study of the inclusion of dihydroquercetin in β-cyclodextrin was carried out. Several quantum chemical parameters were ca...

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...

Water quality modeling in riverine systems is crucial for effective water resource management and pollution mitigation planning. However, the intricate interplay of anthropogenic activities with hydrological, climatic, and fluvial processes presents significant challenges in developing robust models for predicting water quality parameters. This stu...

Accurate monitoring of dissolved oxygen (DO) levels is critical for stakeholders to effectively safeguard water resources and aquatic ecosystem health. This research presents an innovative data fusion framework based on Bayesian model averaging (BMA) by the combination of several neuroscience models (deep learning methodologies) including multilaye...

Mud-logging data, including drilling data (DD) and gas while drilling (GWD) parameters, are recorded in every oil and gas well drilled and are available for real-time assessment making them more beneficial for prompt assessment of formation assessment than the higher cost data provided by cores and wireline or measurement-while-drilling logs. The n...

Colorectal cancer (CRC) is a form of cancer that impacts both the rectum and colon. Typically, it begins with a small abnormal growth known as a polyp, which can either be non-cancerous or cancerous. Therefore, early detection of colorectal cancer as the second deadliest cancer after lung cancer, can be highly beneficial. Moreover, the standard tre...

In this paper, the study focuses on the forecasting of the Air Quality Index (AQI) using linear regression, random forest, and decision tree regression models in Delhi City. The AQI is a crucial metric for monitoring air quality and provides information on the level of air pollution and its potential health risks. The main research aims to develop...

The most critical wastewater quality indicators (WQIs) for diagnosing the performance of wastewater treatment plants are the biological oxygen demand (BOD) and chemical oxygen demand (COD). Measuring the five-day of biological oxygen demand (BOD5) level in wastewater requires significant consumption of energy and. This research unprecedentedly deve...

This study aimed to improve daily streamflow forecasting by combining machine learning (ML) models with signal decomposition techniques. Four ML models were hybridized with five families of maximum overlap discrete wavelet transforms (MODWTs). The hybrid models were applied to predict daily streamflow at the Bir Ouled Taher station in northern Alge...

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...

The photoelectric formation factor (PEF) is a critical tool in reservoir mineralogical characterization, used for measuring photoelectric absorption at a standardized resolution. This measurement is essential for distinguishing between different rock types and minerals, providing valuable insights into subsurface formations. In recent years, many r...

In the context of ongoing environmental changes, particularly against the backdrop of global warming, significant attention is being given to areas of exceptional natural value that, in many aspects, retain a pristine character. One such area is the Biebrza River in northeastern Poland, which, together with the wetlands in its basin, forms one of t...

In recent decades, securing drinkable water sources has become a pressing concern for populations in various regions worldwide. Therefore, to address the growing need for potable water, contemporary water purification technologies can be employed to convert saline sources into drinkable supplies. Therefore, the prediction of important parameters of...

Accurately predicting soil temperature (Ts) serves as the foundation of geothermal applications, modern irrigation strategies in arid agricultural landscapes, and understanding ecosystem changes. Also, this parameter is crucial for estimating crop water requirements, thereby enabling efficient management of scarce water resources in these moisture-...

Study region: Chengdu, Wuhan, Chongqing, and Kunming regions in China. Study focus: Accurate estimation of crop water use or actual evapotranspiration (AET) remains a key obstacle in the effective design of irrigation schedules, plans, and design. This is due to the non-linear nature of this phenomenon. To address this issue and guarantee more accu...

Accurate prediction of daily river flow (Qt) is a challenging task in hydrological modeling, particularly vital for flood mitigation and water resource management. This study introduces an advanced M5 Prime (M5P) predictive model designed to estimate Qt and one- and two-day-ahead river flow forecasts (i.e. Qt+1 and Qt+2). The performance of ensembl...

Recently, explainability and interpretability of machine learning (ML) has been the subject of debate, and improving our understanding of ML response is becoming a challenge as various and significant factors are causing the success or the failure of the ML algorithms in solving a particular task. Undoubtedly, several techniques exist for ML explai...

The accurate prediction of significant wave height is essential for coastal and offshore engineering, and is especially important for producing renewable ocean wave energy. However, Hs is traditionally predicted using empirical or numerical models, which lack accuracy, are computationally demanding, or require extensive datasets. Due to chaotic nat...

This paper proposes new wastewater treatment performances predictive model based on a novel Outlier-robust extreme learning machine (ORELM). The new proposed ORELM model is proposed to resolve some limitations of the standard extreme learning machine (ELM) model and to enhance their performances. The ORELM is than developed for predicting influent...

We propose a new method for predicting daily river water temperature (Tw) using two input variables, namely: (i) air temperature (Ta); and (ii) river discharge (Q). The study was conducted using data collected at two stations operated by the United States Geological Survey (USGS), located at the Missouri River, USA, i.e., Hermann and St. Joseph sta...

The global demand for rare earth elements (REE) is on the rise due to increased need andlimited production sources. This has led to a keen interest in extracting REE from secondarysources like phosphate deposits. During the Upper Cretaceous to Eocene periods, significantaccumulations of REE-rich phosphorite deposits occurred.Using geochemical class...

Petrophysical parameters plays a fundamental aspect in petroleum reservoir characterization, Density is one of the most important of these parameters it has a direct influence on reservoir-fluid volume assessments. In recent years, many research studies have been published on estimating density using artificial intelligence techniques, all these re...

The main determinants of climate change, forest dynamics, land alteration, heat stress, ecological disturbances, and urban expansion. Addressing challenges posed by increasing anthropogenic activities due to population growth and rapid urbanization requires implementing appropriate solutions and fostering greater awareness, leading to improved, hea...

Accurate prediction of reference evapotranspiration (ETo) is crucial for many water-related fields, including crop modelling, hydrologic simulations, irrigation scheduling and sustainable water management. This study compares the performance of different soft computing models such as artificial neural network (ANN), wavelet-coupled ANN (WANN), adap...

The global market for rare earth elements (REE) is growing rapidly, driven by rising demand and limited production sources, prompting interest in recovering REE from secondary sources such as phosphate deposits. The Tethyan belt, extending across North Africa and the Middle East contains substantial Upper Cretaceous to Eocene REE-rich phosphorite d...

Monthly streamflow forecasting is critical for improving water resource management. In this study, several base-classifier data-mining algorithms – conjunctive rule (CR), isotonic regression (ISOR), sequential minimal optimization regression (SMOR) – as well as several hybrid data-mining techniques – disjoint aggregating or dagging (DA)-CR, DA-ISOR...

River water quality management and monitoring are essential responsibilities for communities near rivers. Government decision-makers should monitor important quality factors like temperature, dissolved oxygen (DO), pH, and biochemical oxygen demand (BOD). Among water quality parameters, the BOD throughout 5 days is an important index that must be d...

Currently, the Water Quality Index (WQI) model becomes a widely used tool to evaluate surface water quality for agriculture, domestic and industrial. WQI is one of the simplest mathematical tools that can assist water operator in decision making in assessing the quality of water and it is widely used in the last years. The water quality analysis an...

This study investigates monthly streamflow modeling at Kale and Durucasu stations in the Black Sea Region of Turkey using remote sensing data. The analysis incorporates key meteorological variables, including air temperature, relative humidity, soil wetness, wind speed, and precipitation. The study also investigates the accuracy of multivariate ada...

Total dissolved gas (TDG) concentration plays an important role in the control of the aquatic life. Elevated TDG can cause gas-bubble trauma in fish (GBT). Therefore, controlling TDG fluctuation has become of great importance for different disciplines of surface water environmental engineering.. Nowadays, direct estimation of TDG is expensive and t...

Artificial intelligence (AI) algorithms can potentially contribute to optimizing energy and exergy outputs in renewable resources to increase efficiencies and reduce environmental risk. This study utilized tree-based, linear, and non-linear regression techniques to predict the energy and exergy efficiency of Parabolic Trough Solar Collectors (PTSCs...

The river stage is certainly an important indicator of how the water level fluctuates overtime. Continuous control of the water stage can help build an early warning indicator of floods along rivers and streams. Hence, forecasting river stages up to several days in advance is very important and constitutes a challenging task. Over the past few deca...

Pan evaporation modeling and forecasting are needed to provide timely, continuous, and valuable information to support water management. This study aimed to overcome the constraints identified in traditional regression techniques and less explored machine learning models—the CatBoost, to enhance the precision and comprehensiveness of pan evaporatio...

This study proposes a new framework to predict suspended sediment load (Qssl) by implementing tree-based algorithms, including Random Tree (RT), Random Forest (RF), and M5Prime (M5P). Several combinations of input factors, i.e., rainfall, flow discharge, and previous Qssl, are fed to the models and the best input scenario is constructed. The result...

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....

Changes in soil temperature (ST) play an important role in the main mechanisms within the soil, including biological and chemical activities. For instance, they affect the microbial community composition, the speed at which soil organic matter breaks down and becomes minerals. Moreover, the growth and physiological activity of plants are directly i...

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...

Over the centuries, extensive changes have occurred in the functioning of the hydrosphere. In the case of Poland, the hydrographic network has been significantly transformed, and many of its elements have ceased to exist. The aim of this study was to investigate renaturalised lakes and to determine their original volume, which is a fundamental para...

In this study, the vote algorithm used to improve the performances of three machine-learning models including M5Prime (M5P), random forest (RF), and random tree (RT) is developed (i.e. V-M5P, V-RF, and V-RT). Developed models were tested for forecasting soil temperature (TS) at 1, 2, and 3 days ahead at depths of 5 and 50 cm. All models were develo...

At the outbreak of infectious diseases, the response of different communities to the disease varies, and children are most affected by the collective anxiety and grief that consequently arises. In this research, the behavior of children and their parents in terms of hygiene and precautions before and during the COVID-19 pandemic was investigated. T...

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...

Magnetic water treatment (magnetic treatment device (MTD)) has long been a contentious procedure for domestic water treatment. This study examines the pros and cons of using different water types with and without a magnetic field treatment for growing French bean crop irrigation. The MTD used in this experiment works by ionizing the dissolved solid...

Accurate information about the wetted soil dimensions of soil under surface drip irrigation can assist the designers to determine and for selecting the ideal emitter discharge rates and spacings to reduce system equipment costs and provide improved soil water conditions for the most efficient and effective use of water. Temporal movement of wetting...

Air relative humidity (RH) is one of the most important meteorological variables for hydrological and climatic studies, and their accurate and reliable prediction is of great importance in all fields concerning global climate change, and it is helpful in making critical decisions. This presentation proposes a hybrid air relative humidity prediction...

Total phosphorus (T-P) refers to the concentration of phosphorus in water and is one of the important parameters for eutrophication in lakes and rivers. In current research, neuroscience dependent (i.e., singular and double-platform synthetic) approaches were employed to predict the river T-P concentration. Singular techniques were developed utiliz...

Lakes are an important element of the hydrosphere that contribute to the stabilisation of water circulation by providing biodiversity conditions or supporting the development of different branches of the economy. All these properties depend on the longevity of lakes in the environment and the processes related to their evolution. Based on archival...

In light of recent improvements in flood susceptibility mapping using machine learning models, there remains a lack of research focusing on employing ensemble algorithms like Light Gradient Boosting on Elastic-net Predictions (Light GBM) and Elastic-net Classi-fier (L2/Binomial Deviance) for mapping flood susceptibility in Qaa'Jahran, Yemen. This s...

This study compares the ability of Long Short-Term Memory (LSTM) tuned with Grey Wolf Optimization (GWO) and machine learning models, artificial neural network (ANN), Adaptive-Network-based Fuzzy Inference System (ANFIS), and support vector machine (SVM) enhanced with GWO in the prediction of monthly streamflow. Precipitation, temperature, and stre...

Formation characteristics are a crucial requirement for reservoir modeling and they need to be constantly updated as new static and/or dynamic reservoir-property details become available. This improves reservoir interpretation while achieving a good history-matching for a better representation of the reservoir. This paper proposes a new method to e...

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...

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...

Long-term gridded climate data, NASA Prediction of Worldwide Energy Resources (NASA POWER) and the fifth generation of European ReAnalysis Land Component (ERA5-Land), are important alternatives to gauges, but their effectiveness in capturing tropical precipitation and temperature extremes has not been thoroughly studied. Therefore, this study aims...

Reliable and precise estimation of solar energy as one of the green, clean, renewable and inexhaustible types of energies can play a vital role in energy management, especially in developing countries. Also, solar energy has less impact on the earth’s atmosphere and environment and can help to lessen the negative effects of climate change by loweri...

The likelihood of surface water and groundwater contamination is higher in regions close to landfills due to the possibility of leachate percolation, which is a potential source of pollution. Therefore, proposing a reliable framework for monitoring leachate and groundwater parameters is an essential task for the managers and authorities of water qu...

This study searches the feasibility of a new hybrid extreme leaning machine tuned with improved reptile search algorithm (ELM-IRSA), in river flow modeling. The outcomes of the new method were compared with single ELM and hybrid ELM-based methods including ELM with salp swarm algorithm (SSA), ELM with equilibrium optimizer (EO) and ELM with reptile...

Climatic condition is triggering human health emergencies and earth’s surface changes. Anthropogenic activities, such as built‑up expansion, transportation development, industrial works, and some extreme phases, are the main reason for climate change and global warming. Air pollutants are increased gradually due to anthropogenic activities and trig...

Despite the high importance of coagulation process in drinking water treatment plant (DWTP), challenge remains in effectively linking raw water quality measured at the inlet of the DWTP with coagulant dosage rate. This study proposes an integral modelling framework using hybrid extreme learning machine and Bat metaheuristic algorithm (ELM-Bat) for...

Accurate and sustainable management of water resources is among the most important circumstances of basin and river engineering. In this study, a hybrid machine learning (ML) model was generated using CatBoost and Genetic Algorithm (GA) for significant impact on river flow prediction. The study was applied to Sakarya Basin, which is located in semi...

Trash mulches are remarkably effective in preventing soil erosion, reducing runoff-sediment transport-erosion, and increasing infiltration. The study was carried out to observe the sediment outflow from sugar cane leaf (trash) mulch treatments at selected land slopes under simulated rainfall conditions using a rainfall simulator of size 10 m × 1...

This paper proposes a hybrid air relative humidity prediction based on preprocessing signal decomposition. New modelling strategy was introduced based on the use of the empirical mode decomposition, variational mode decomposition, and the empirical wavelet transform, combined with standalone machine learning to increase their numerical performances...

Characterized by their high spatiotemporal variability, rainfalls are hard to predict, especially under climate change. This study proposes a multilayer perceptron (MLP) network optimized by Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Firefly Algorithm (FF), and Teleconnection Pattern Indices - such as North Atlantic Oscillation (NAO...

Trash mulches are remarkably effective in preventing soil erosion, reducing runoff- sediment transport-erosion, and increasing infiltration. The study was carried out to observe the sediment outflow from sugar cane leaf (trash) mulch treatments at selected land slopes under simulated rainfall conditions using a rainfall simulator of size 10 m × 1.2...

Considering the importance of nitrogen and organic carbon in supporting the growth of various algae and organic matters, that improves eutrophication along the water bodies. It is, therefore, essential to develop reliable tools that can help policymakers and experts in-understand the environmental and aquatic importance of these macronutrients in r...

This study investigates the feasibility of a relevance vector machine tuned with improved Manta-Ray foraging optimization (RVM-IMRFO) in predicting monthly pan evaporation using limited climatic input data (e.g. temperature). The accuracy of the RVM-IMRFO was evaluated by comparing it with RVM tuned by gray wolf optimization, RVM tuned with a whale...

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...