Journal of Food, Agriculture and Environment

Review on fuzzy water quality index for surface water with artificial neural network
 

Author(s):

Sarita Jibhau Wagh, G. M. Ponde, M. D. Sangale

Recieved Date: 2022-08-06, Accepted Date: 2022-09-27

Abstract:

Our water quality is still being impacted by human activity, which is a big issue on a global scale. Since the 1960s, the worldwide state of surface water and groundwater systems’ overall water quality has been determined using a critical water quality index (WQI) technique. This analysis will aid in choosing a water quality index model to assess the quality of water used for drinking, irrigation, residential usage, and industrial use. Monitoring the quality of groundwater around drilling sites is essential for safeguarding water supplies. The acquired characteristics were used to calculate the index for water quality. The goal of this review is to evaluate how well Artificial Neural Network (ANN) approaches can simulate the groundwater water quality index. A multiple regression model was used to produce the seven ANN input parameters that were optimized and constrained. The optimal parameters (electrical conductivity, pH, calcium, magnesium, and sodium ions) were determined for a network with five input neurons in addition to five neurons in the hidden layer. Thus, especially in industrial settings, neural network models may be utilized to directly forecast the quality of groundwater. This suggested approach, which makes use of cutting-edge artificial intelligence, can help with water management and treatment.

Keywords:

Artificial Neural Network (ANN), Canadian Council of Minister environment (CCME), Fuzzy Water Quality Index (FWQI), Water Quality Index (WQI)

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Journal: Journal of Food, Agriculture and Environment
Year: 2022
Volume: 20
Issue: 3&4
Category: Environment
Pages: 113-118

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