Simulation of the Nitrate Concentrations in Consumed Well Water Using the Error Gradient Backpropagation Neural Network: A Case Study: M’bahiakro (Central-Eastern Ivory Coast)

• Published in: Environmental modeling & assessment 2024-10, Vol.29 (5), p.855-869
• Main Authors: N’cho, Hervé Achié, Koffi, Kouadio, Konan, Séraphin Kouakou, Baï, Ruth, Kouame, Innocent Kouassi, Kouassi, Lazare Kouakou
• Format: Article
• Language: English
• Subjects: Applications of Mathematics; Artificial neural networks; Back propagation networks; Datasets; Dissolved oxygen; Drinking water; Earth and Environmental Science; Electrical conductivity; Electrical resistivity; Environment; Error analysis; Math. Appl. in Environmental Science; Mathematical Modeling and Industrial Mathematics; Mathematical models; Neural networks; Nitrates; Operations Research/Decision Theory; Performance evaluation; Rainy season; Redox potential; Water depth; Water table; Well water
• ISSN: 1420-2026; 1573-2967
• DOI: 10.1007/s10666-024-09970-0

In M’bahiakro, nitrate contamination of drinking well water is becoming a cause for concern and continues despite the efforts made in the town. To help monitor these waters, this study aims to predict nitrate concentrations in the M’bahiakro water table based on physico-chemical parameters measured in situ. To this end, a gradient error back-propagation (BPNN) artificial neural network (ANN) was developed to simulate nitrate concentrations using temperature (T), electrical conductivity (EC), dissolved oxygen (O 2 ), redox potential (Eh) and well water depth as input data. The resulting dataset was divided into two parts to form the artificial neural network, where 70% of the dataset was used for training, and the remaining 30% was also divided into two equal parts: one for testing and the other for model validation. The models were configured using a constructive approach, which consists of testing each input variable individually in a reference network and combining the variables until the best intelligent model is obtained according to the chosen performance criteria. The intelligent models obtained were evaluated on the basis of the coefficient of determination ( R 2 ) closest to 1 and the lowest mean square error (MSE). The results obtained showed that the BPNN models developed using four input variables in the dry and rainy seasons provided the best results. The MSE and R 2 values were around 0.01 mg/L and 95%, respectively. They are obviously more accurate since the mean square errors are low with coefficients of determination close to unity. The BPNN models thus obtained were able to reproduce satisfactorily the nitrate concentrations obtained experimentally in 19 wells in the town of M’bahiakro. However, it is essential to continue this study in order to define the time interval during which the BPNN models obtained can remain valid in terms of performance in the M’bahiakro area.