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Performance of pozzolan in trickling filter for secondary treatment of urban wastewater in Tlemcen, Algeria

Hamidi Amina, Belarbi Fadila, Bouchelkia Hamid, Bouchenak Khelladi Racha M.

Journal of Water and Land Development

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2022

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no. 55

166--177

EN

The research aims to study the purification performance of a local and natural material as an input or as a biological filter for treating urban domestic wastewater. For this purpose, pozzolan was used as the biofiltration support that was provided from Beni Saf located in the North-West of Algeria. Tests were carried out with a specially modified pilot unit (TE900) for wastewater treatment over a period of four months. To assess the efficiency of the treatment, two main parameters have been focused on - the height of the sprinkler filter (40 cm and 80 cm) and the flow rate (10, 16, and 25 dm3∙h-1). Physicochemical and bacteriological analyses were carried out on raw wastewater and treated water. The obtained results show that the Beni Saf pozzolan-filled trickling filter eliminates a large fraction of the studied pollutants. The purification yields obtained are fairly encouraging; 98% for turbidity, 88% for suspended solids (SS), 94% for chemical oxygen demand (COD), and 98% for biological oxygen demand (BOD5 ). As for bacterial indicators, the formation of biofilms has significantly reduced bacterial activity with a percentage of over 80%. It can be concluded that the reduction of pollutant parameters clearly indicates the effectiveness of the treatment by this ecological process. Therefore, the use of local and natural materials for wastewater treatment can be a promising alternative based on sustainable environmental technologies and development.

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Evaluating annual and seasonal patterns of suspended sediment loads in a semi-arid watershed, central Algeria

Rabah Mosbah, Bouchelkia Hamid, Belarbi Fadila, Millares Agustin

Journal of Water and Land Development

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2021

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no. 50

158--172

EN

Soil loss is a major problem for watersheds management in semi-arid environments. The objective of the present study is to analyze the annual and seasonal patterns of suspended loads and quantify the specific sediment yields in a semi-arid environment of the Mazafran Watershed in central Algeria. The obtained information of water discharge and suspended sediment load, recorded during 19 years, was confronted with precipitation data in order to establish the relationships between theforcing agents and erosive processes. The specific sediment yield was estimated by assessing rating curve data under two types of identified responses. The obtained results allowedconfirming the seasonality on suspended sediment transport in the studied basin, which accounts for 56% of the total suspended sediment load estimated in winter. The mean annual suspended sediment is estimated at 17.52 Mg∙ha–1·y–1. The results highlighted that the type 2 event dominates the production of sediment in the study area in comparison with type 1 event. The analysis of the variability of rainfall erosivity index showed that there is a strong correlation between the annual precipitation and modified Fournier index (MFI), and a weak correlation with the monthly precipitation concentration index (PCI). Moreover, the spatial distribution of the modified Fournier index at the basin scale showed the highest precipitation aggressiveness in the Southern part of the study region for both type of events, whereas the precipitation aggressiveness low to moderate in the remaining part of the study region.

3

Novel hybrid approaches based on evolutionary strategy for streamfow forecasting in the Chellif River, Algeria

Zakhrouf Mousaab, Bouchelkia Hamid, Stamboul Madani, Kim Sungwon

Acta Geophysica

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2020

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Vol. 68, no. 1

167--180

EN

In this study, the feedforward neural networks (FFNNs) were proposed to forecast the multi-day-ahead streamfow. The parameters of FFNNs model were optimized utilizing genetic algorithm (GA). Moreover, discrete wavelet transform was utilized to enhance the accuracy of FFNNs model’s forecasting. Therefore, the wavelet-based feedforward neural networks (WFFNNs-GA) model was developed for the multi-day-ahead streamfow forecasting based on three evolutionary strategies [i.e., multi-input multi-output (MIMO), multi-input single-output (MISO), and multi-input several multi-output (MISMO)]. In addition, the developed models were evaluated utilizing fve diferent statistical indices including root mean squared error, signal-to-noise ratio, correlation coefcient, Nash–Sutclife efciency, and peak fow criteria. Results provided that the statistical values of WFFNNs-GA model based on MISMO evolutionary strategy were superior to those of WFFNNs-GA model based on MISO and MIMO evolutionary strategies for the multi-day-ahead streamfow forecasting. Results indicated that the performance of WFFNNs-GA model based on MISMO evolutionary strategy provided the best accuracy. Results also explained that the hybrid model suggested better performance compared with stand-alone model based on the corresponding evolutionary strategies. Therefore, the hybrid model can be an efcient and robust implement to forecast the multi-day-ahead streamfow in the Chellif River, Algeria.

4

Hydrological modeling using the SWAT model based on two types of data from the watershed of Beni Haroun dam, Algeria

Kateb Zakaria, Bouchelkia Hamid, Benmansour Abdelhalim, Belarbi Fadila

Journal of Water and Land Development

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2019

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no. 43

76--89

EN

The dam of Beni Haroun is the largest in Algeria, and its transfer structures feed seven provinces (wilayas) in the eastern part of Algeria. Due to its importance in the region, it has now become urgent to study its watershed as well as all the parameters that can influence the water and solid intakes that come into the dam. The Soil and Water Assessment Tool (SWAT) model is used to quantify the water yields and identify the vulnerable spots using two scenarios. The first one uses worldwide data (GlobCover and HWSD), and the second one employs remote sensing and digital soil mapping in order to determine the most suitable data to obtain the best results. The SWAT model can be used to reproduce the hydrological cycle within the watershed. Concerning the first scenario, during the calibration period, R2 was found between 0.45 and 0.69, and the Nash–Sutcliffe efficiency (NSE) coefficient was within the interval from 0.63 to 0.80; in the validation period, R2 lied between 0.47 and 0.59, and the NSE coefficient ranged from 0.58 to 0.64. As for the second scenario, during the calibration period, R2 was between 0.60 and 0.66, and the NSE coefficient was between 0.55 and 0.75; however, during the validation period, R2 was in the interval from 0.56 to 0.70, and the NSE coefficient within the range 0.64–0.70. These findings indicate that the data obtained using remote sensing and digital soil mapping provide a better representation of the watershed and give a better hydrological modelling.

PL

Beni Haroun jest największym zbiornikiem zaporowym Algierii zasilającym w wodę siedem prowincji we wschodniej części kraju. Podjęcie badań jego zlewni oraz wszystkich czynników, które wpływają na dostawę wody i zawiesiny do zbiornika, okazało się pilne ze względu na regionalne znaczenie zbiornika. Model SWAT (Soil and Water Assessment Tool) wykorzystano do ilościowego ujęcia natężenia przepływu wody i identyfikacji wrażliwych elementów systemu z użyciem dwóch scenariuszy. W pierwszym wykorzystano dane światowe, w drugim dane z teledetekcji i cyfrowych map glebowych celem ustalenia najbardziej odpowiednich danych do osiągnięcia najlepszych rezultatów. Model SWAT można użyć do odtworzenia cyklu hydrologicznego na obszarze zlewni. Według pierwszego scenariusza podczas kalibracji R2 wynosił od 0,45 do 0,69, a współczynnik efektywności Nasha–Sutcliffa (NSE) mieścił się w przedziale od 0,63 do 0,80. Podczas walidacji R2 zmieniał się od 0,47 do 0,59, a współczynnik NSE od 0,58 do 0,64. Według drugiego scenariusza podczas kalibracji R2 wynosił od 0,60 do 0,66, a współczynnik NSE od 0,55 do 0,75. Podczas walidacji współczynniki mieściły się odpowiednio w granicach od 0,56 do 0,70 i od 0,64 do 0,70. Wyniki wskazują, że dane pozyskane z teledetekcji i cyfrowych map glebowych stanowią lepszą reprezentację zlewni i umożliwiają usprawnienie modelowania hydrologicznego.