The effectiveness of Monte Carlo-based neural network in analyzing the performance of pumping wells

Articles in Press

Document Type : Research Paper

Authors

1 Water Engineering Department, Water and Soil Engineering Faculty, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran

2 Water Engineering, Department, University of Zabol, Zabol, Iran

Abstract

The present study was conducted with the aim of evaluating the efficiency of the multilayer perceptron artificial neural networks in estimating the pump rotation of drinking water wells located in Bandar Turkman using the key parameters of pump discharge, pump installation depth, and groundwater level. This study involves an integrated approach of artificial neural networks modeling and its sensitivity analysis as well as Monte-Carlo simulation. The neural networks results showed that this model has an acceptable performance for predicting the pump rotation speed of wells. Furthermore, it was reveled that this model performance is highly affected by seasonal changes. The best performance of this model was achieved in May with R2=0.98and RMSE=15.15 and in March with R2=0.99and RMSE=15.13 for training and testing phases, respectively. The sensitivity analysis of input variables indicated that the groundwater level with 48% importance has the greatest effect on the pump rotation speed, followed by the pump discharge and pump installation depth with 37% and 15%, respectively. The reliability index analysis (β) showed that the seasonal changes have high influence on studied pumping system so that in winter season, it has excellent reliability (β=1.7-2.2) and through summer season, it has negative reliability which indicates the possibility of system failure or excessive energy consumption by pumps. The proposed approach in this study provides a practical framework for water and wastewater managers to optimize pumping operations, reduce energy costs, and enhance water management in arid regions.

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Iranian Journal of Soil and Water Research

Articles in Press, Accepted Manuscript
Available Online from 23 April 2025

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