Prediction of nitrate concentrations using multiple linear regression and radial basis function neural network in the Cheliff River basin, Algeria

被引：2

作者：

Mehdaoui, Ibrahim ^{[1
]}

Boudibi, Samir ^{[2
]}

Latif, Sarmad Dashti ^{[3
]}

Sakaa, Bachir ^{[1
,2
]}

Chaffai, Hicham ^{[1
]}

Hani, Azzedine ^{[1
]}

机构：

[1] Badji Mokhtar Univ, Fac Earth Sci, Lab Water Resources & Sustainable Dev, Annaba, Algeria

[2] Campus Mohamed Khider Univ, Sci & Tech Res Ctr Arid Reg CRSTRA, Biskra, Algeria

[3] Komar Univ Sci & Technol, Coll Engn, Civil Engn Dept, Sulaimany, Iraq

来源：

JOURNAL OF APPLIED WATER ENGINEERING AND RESEARCH | 2024年 / 12卷 / 01期

关键词：

Nitrate concentration; radial basis function neural network model; multiple linear regression model; water reservoir; Upper-Cheliff River basin; DISSOLVED-OXYGEN CONTENT; WATER-QUALITY PARAMETERS; DANUBE RIVER; MACHINES; MODEL;

D O I：

10.1080/23249676.2023.2207838

中图分类号：

TV21 [水资源调查与水利规划];

学科分类号：

081501 ;

摘要：

In this paper, multiple linear regression (MLR) and radial basis function neural network (RBF-NN) are applied to predict nitrate (NO3-) concentration with and without reservoir volume (WV) as predictor using monthly data for ten years in three water reservoirs located in the upper Cheliff basin (NW of Algeria). The datasets were divided into training (80%) and testing (20%) sets and two different scenarios were compared. The results revealed that RBF-NN was more efficient (MAE = 0.192 and SI = 0.061) compared with the MLR model to predict NO3- in all reservoirs. RBF-NN provided the best accuracy in the testing period with a high R-2 of 0.957 in reservoir II, and low MSE and PBias of 0.048 mg/l and 2.98% in the training period in reservoir III, respectively. Overall, the best results were generated by M(iii) in scenario B.

引用

页码：77 / 89

页数：13

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