Application of Gene Expression Programming Method in Estimation of Shannon Entropy Water Quality Index

Water quality, due to its vital role in sustaining life and supporting various aspects of human life, is a key issue in water resource management. With industrial advancements and increasing water demands, water pollution has become a serious concern. This research investigates the water quality of the Zohreh River using the Shannon Entropy Water Quality Index (SEWQI) and Gene Expression Programming (GEP) model. Data on water quality parameters, including pH, electrical conductivity, phosphate, nitrate, ammonium, total hardness, fecal coliform, dissolved oxygen, BOD5, COD, and turbidity, were collected from the Firoozabad and 720-meter Suire stations over a ten-year period (2011-2020). Firstly, the entropy of each parameter was calculated, and their weights were determined. Then, using the calculated weights, the Shannon Entropy Water Quality Index was obtained. The results obtained from the GEP model showed that this method has a high accuracy in predicting the water quality index with a coefficient of determination (R²) of 0.803 and root mean square error (RMSE) of 0.07. The results of this study highlight the use of GEP as an effective tool for assessing and managing water quality in the Zohreh River and can contribute to improving water resource management policies and preventing risks to public health and ecosystems.Firstly, the entropy of each parameter was calculated, and their weights were determined. Then, using the calculated weights, the Shannon Entropy Water Quality Index was obtained. The results obtained from the GEP model showed that this method has a high accuracy in predicting the water quality index with a coefficient of determination (R²) of 0.803 and root mean square error (RMSE) of 0.07. The results of this study highlight the use of GEP as an effective tool for assessing and managing water quality in the Zohreh River and can contribute to improving water resource management policies and preventing risks to public health and ecosystems.