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Artificial Neural Networks vs Long Short-Term Memory Prediction of Solid Flow in Tafna Basin (North-West Algeria)

Medfouni Mohamed Nadjib, Korichi Khaled, Marouf Nadir

Ecological Engineering & Environmental Technology

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2024

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Vol. 25, iss. 3

275--286

EN

The main objective of this work is to select the most reliable machine learning model to predict the generated solid flow in the Tafna basin (North-West of Algeria). It is about the artificial neural networks (ANN) and long short-term memory (LSTM). The sediment load is recorded through three hydrometric stations. The efficiency and performance of the two models is verified using the correlation coefficient (R2), the Nash-Sutcliffe coefficient (NSC) and the root mean square error (RMSE). The obtained simulated solids load shows a very good correlation in terms of precision although the ANN model gave relatively better results compared to the LSTM model where low RMSE values were recorded, which confirms that the artificial intelligence models remain also effective for the treatment and the prediction of hydrological phenomena such as the estimation of the solid load in a such watershed.

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Assessment of soil erosion using the GIS-based erosion potential method in the Kebir Rhumel Watershed, Northeast Algeria

Zeghmar Amer, Marouf Nadir, Mokhtari Elhadj

Journal of Water and Land Development

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2022

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no. 52

133--144

EN

Soil erosion is an important factor that should be considered when planning renewable natural resource projects, effects of which can be measured by modelling techniques. Therefore, disintegration models determine soil loss intensity and support soil conservation practices. This study estimates soil loss rates by water erosion using the Erosion Potential Method (EPM) in the Kebir Rhumel Watershed located in Northeast Algeria. The area is north to south sub-humid to semi-arid, receives irregular rainfall, and has steep slopes and low vegetation cover which makes it very vulnerable to erosion. The main factors in the EPM (soil erodibility, soil protection, slope, temperature, and rainfall) were evaluated using the Geographical Information System (GIS) and data provided by remote sensing technologies. The erosion intensity coefficient Z was 0.60, which indicates medium erosion intensity. While the results showed the average annual soil erosion of 17.92 Mg∙ha-1∙y -1, maximum and minimum losses are 190.50 Mg∙ha-1∙y-1 and 0.21 Mg∙ha-1∙y-1, respectively. The EPM model shows satisfactory results compared to some studies done in the basin, where the obtained results can be used for more appropriate management of land and water resources, sustainable planning, and environmental protection.

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The assessment of artificial neural network rainfall-runoff models under different input meteorological parameters. Case study: Seybouse basin, Northeast Algeria

Aoulmi Yamina, Marouf Nadir, Amireche Mohamed

Journal of Water and Land Development

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2021

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no. 50

38--47

EN

Over the past two decades, artificial neural networks (ANN) have exhibited a significant progress in predicting and modeling non-linear hydrological applications, such as the rainfall-runoff process which can provide useful contribution to water resources planning and management. This research aims to test the practicability of using ANNs with various input configurations to model the rainfall-runoff relationship in the Seybouse basin located in a semi-arid region in Algeria. Initially, the ANNs were developed for six sub-basins, and then for the complete watershed, considering four different input configurations. The 1st (ANN IP) considers only precipitation as an input variable for the daily flow simulation. The 2nd (ANN II) considers the 2nd variable in the model input with precipitation; it is one of the meteorological parameters (evapotranspiration, temperature, humidity, or wind speed). The third (ANN IIIP,T,HUM) considers a combination of temperature, humidity, and precipitation. The last (ANN VP,ET,T,HUM,Vw) consists in collating different meteorological parameters with precipitation as an input variable. ANN models are made for the whole basin with the same configurations as specified above. Better flow simulations were provided by (ANN IIP,T) and (ANN IIP,Vw) for the two stations of Medjez-Amar II and Bordj-Sabath, respectively. However, the (ANN VP,ET,T,HUM,Vw)’s application for the other stations and also for the entire basin reflects a strategy for the flow simulation and shows enhancement in the prediction accuracy over the other models studied. This has shown and confirmed that the more input variables, as more efficient the ANN model is.

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Impact study of Beni-Haroun dam on the environmental and socio-economic elements in Kébir-Rhumel basin, Algeria

Marouf Nadir, Remini Boualem

Journal of Water and Land Development

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2019

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no. 43

120--132

EN

Surface water of Kébir Rhumel basin is indispensable for domestic and industrial needs of this region. Industrial development, with water excessive use and chemical products, in production and industrial treatment, and not sustainable fertilizers in agriculture, constitutes a serious threat to maintain our resources of good water quality. The majority of domestic and industrial wastewaters of the region, discharged to the stream water of Kébir Rhumel basin, promote the water enrichment in nutritious elements, phosphorus and nitrogen and particularly, the resulting increase in the aquatic primary production, mainly the planktonic or benthic algae. As a result, the physical and chemical properties of water deteriorate. This basin allows construction of the largest dam in Algeria “Beni-Haroun dam”. The infrastructure that was one of the greatest challenges of Algeria is now a reality. Hydraulic complex of Beni-Haroun remains a strategic and major achievement in the development program of water resources sector. This enormous building was constructed in the territory of the Wilaya (province) of Mila, used to meet water needs, with four million inhabitants, of eastern Algeria and other neighbouring regions that have suffered from lack of water consumption, especially in summer. In addition, it will irrigate over 42 000 ha, going thus to the several plains. Integration of sociological and environmental concerns into dams design is a recent phenomenon. It is considered at the impact study level, during which the dam study project is accompanied by a survey to assess project impact on natural environment and socioeconomic development.

PL

Wody powierzchniowe na terenie basenu Kébir-Rhumel są niezbędne do zaspokojenia domowych i przemysłowych potrzeb regionu. Rozwój przemysłowy z jego nadmiernym zużyciem wody i substancji chemicznych w procesie produkcji i przetwarzania oraz niezrównoważone stosowanie nawozów w rolnictwie stwarzają poważne zagrożenie dla jakości wody. Większość domowych i przemysłowych ścieków z regionu jest odprowadzana do wód płynących basenu Kébir-Rhumel, powodując ich wzbogacenie w pierwiastki biogenne (fosfor i azot), co skutkuje zwiększoną produkcją pierwotną głównie planktonowych i osiadłych glonów. W wyniku tego pogarsza się jakość wody. Układ basenu umożliwił zbudowanie największej zapory w Algierii – zapory Beni-Haroun. Ten obiekt infrastruktury wodnej, będący jednym z największych wyzwań Algierii, stał się rzeczywistością. Kompleks wodny Beni-Haroun jest strategiczym i głównym osiągnięciem programu rozwoju zasobów wodnych. Ta ogromna budowla umieszczona w prowincji Mila zaspokaja potrzeby 4 mln mieszkańców wschodniej Algierii i sąsiadujących regionów, które cierpiały na brak wody pitnej, szczególnie latem. Ponadto wodą ze zbiornika będzie się nawadniać 42 000 ha na kilku równinach. Zagadnieniem ostatnio branym pod uwagę podczas projektowania zapór jest integrowanie problemów społecznych i środowiskowych. Te problemy są rozważane na etapie oceny oddziaływania na środowisko, w którym projektowaniu zapory towarzyszy analiza wpływu projektu na środowisko naturalne i rozwój społeczno-gospodarczy.