Larbi Houichi

Modeling daily reference evapotranspiration (ET 0 ) in the north of Algeria using generalized regression neural networks (GRNN) and radial basis function neural networks (RBFNN): a comparative study

Estimation of reference evapotranspiration (ET0) is needed to support irrigation design and scheduling, and watershed hydrology studies. There are many available methods to estimate evapotranspiration from a water surface, comprising both direct and indirect methods. In the first part of this study, the generalized regression neural networks model (GRNN) and radial basis function neural network (RBFNN) are developed and compared in order to estimate the reference ET0 for the first time in Algeria. Various daily climatic data, that is, daily mean relative humidity, sunshine duration, maximum, minimum and mean air temperature, and wind speed from Dar El Beida, Algiers, Algeria, are used as inputs to the GRNN and RBFNN models to estimate the ET0 obtained using the FAO-56 Penman-Monteith equation (PM56). The performances of the models are evaluated using root mean square errors (RMSE), mean absolute error (MAE), Willmott index of agreement (d) and correlation coefficient (CC) statistics. In the second part of the study, the empirical Hargreaves-Samani (HG) and Priestley-Taylor (PT) equations are also considered for the comparison. Based on the comparisons, the GRNN was found to perform better than the RBFNN, Priestley-Taylor and Hargreaves-Samani models. The RBFNN model is ranked as the second best model.

New equation for the computation of flow velocity in partially filled pipes arranged in parallel

This paper presents a new approach for the computation of flow velocity in pipes arranged in parallel based on an analytic development. The estimation of the flow parameters using existing methods requires trial and error procedures. The assessment of flow velocity is of great importance in flow measurement methods and in the design of drainage networks, among others. In drainage network design, the flow is mostly of free surface type. A new method is developed to eliminate the need for trial methods, where the computation of the flow velocity becomes easy, simple, and direct with zero deviation compared to Manning equation results and other approaches such as that have been considered as the best existing solutions. This research work shows that these approaches lack accuracy and do not cover the entire range of flow surface angles: 0° ≤ θ ≤ 360°.

Analytical Solution for the Flow Velocity and Water Surface Angle in drainage and Sewer networks: Case of Pipes arranged in series

Drainage and sewer network runs mostly under free surface flow condition. Among the characteristics which are important for practitioners are the flow velocity and water surface angle. The computation of these parameters in partially full pipes using Manning equation is implicit and requires iterative and laborious calculation methods. The goal of this paper is to provide a new method, where the exact computation of the flow velocity and water surface angle in partially filled pipe becomes easy, direct and simple using a reference pipe with known characteristics.

Evolving connectionist systems (ECoSs): a new approach for modeling daily reference evapotranspiration (ET0)

Over the last few years, the uses of artificial intelligence techniques (AI) for modeling daily reference evapotranspiration (ET0) have become more popular and a considerable amount of models were successfully applied to the problem. Therefore, in the present paper, we propose a new evolving connectionist (ECoS) approaches for modeling daily reference evapotranspiration (ET0) in the Mediterranean region of Algeria. Three ECoS models, namely, (i) the off-line dynamic evolving neural-fuzzy inference system called DEFNIS_OF, (ii) the on-line dynamic evolving neural-fuzzy inference system called DEFNIS_ON, and (iii) the evolving fuzzy neural network called (EFuNN), were statistically compared using the root mean square error (RMSE), the mean absolute error (MAE), the coefficient of correlation (R), and the Nash-Sutcliffe efficiency (NSE) indexes. The proposed approaches were applied for modeling daily ET0 using climatic variables from two weather stations: Algiers and Skikda, Algeria. Five well-known climatic variables were selected as inputs: daily maximum and minimum air temperatures (Tmax and Tmin), daily wind speed (WS), daily relative humidity (RH), and daily sunshine hours (SH). The effect of combining several climatic variables as inputs was evaluated, and at least six scenarios were developed and compared. The proposed ECoS models were compared against the reference Penman-Monteith model referred as “FAO-56 PM”. According to the results obtained, the DEFNIS_OF1 model having Tmax, Tmin, WS, RH, and SH as inputs, is the best model, followed by the DEFNIS_ON1, and the EFuNN1 is the worst model. The R and NSE value calculated for the testing dataset for the Algiers and Skikda stations were (0.954, 0.910) and (0.954, 0.905), respectively. While both DEFNIS_OF1 and DEFNIS_ON1 showed good accuracy and high performances, the EFuNN1 was less accurate.

Water quality index assessment of Koudiat Medouar Reservoir, northeast Algeria using weighted arithmetic index method

Abstract Water quality index (WQI) is a mathematical tool used to transform large quantities of water quality data into a single number which present water quality level. The aim of the present study is to evaluate the quality of Koudiat Medouar Dam in Batna (Algeria) to assess its suitability for drinking purposes. Samples were assessed for ten (10) physicochemical settings namely pH, electrical conductivity, total hardness, nitrate, sulphate, chloride, calcium, magnesium, dissolved oxygen and turbidity. The calculation of WQI was done via weighted arithmetic index method. The WQI values ranged from 99.097 to 174.92 during 2015. It reflected that the water samples were in February in the range of very poor quality and ranged to be in unsuitable for drinking rang in the all other months. The WQI of the present study reveals dam water is contaminated and not suitable for drinking purpose without giving treatment.