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Updated: Sep 2, 2025

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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Improving short-term water demand forecasting using evolutionary algorithms.

Justyna Stańczyk1, Joanna Kajewska-Szkudlarek2, Piotr Lipiński3

  • 1Institute of Environmental Engineering, Wroclaw University of Environmental and Life Sciences, 24 Grunwaldzki Square, 50-363, Wrocław, Poland.

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Accurate water demand forecasting is crucial for water systems. This study introduces a linear regression model with evolutionary strategies, achieving under 2% MAPE error for short-term water consumption prediction.

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Area of Science:

  • Environmental Engineering
  • Water Resource Management
  • Data Science

Background:

  • Effective water distribution systems rely on monitoring water quality and quantity.
  • Water demand forecasting (WDF) systems are essential for managing water resources.
  • Accurate forecasting requires accounting for random factors like weather and human activity.

Purpose of the Study:

  • To develop an advanced approach for forecasting water demand.
  • To improve the accuracy of short-term water consumption predictions.
  • To integrate evolutionary strategies with linear regression for enhanced WDF.

Main Methods:

  • A novel approach combining linear regression with evolutionary strategies for seasonality extraction.
  • Comparative analysis against Support Vector Regression (SVR), Multilayer Perceptron (MLP), and Random Forest (RF).
  • Implementation of a daily forecasting procedure at the District Metered Area (DMA) level.

Main Results:

  • The proposed model achieved a Mean Absolute Percentage Error (MAPE) below 2% for daily water consumption.
  • Demonstrated superior performance compared to SVR, MLP, and RF in forecasting accuracy.
  • Successfully extracted weekly seasonality patterns influencing water demand.

Conclusions:

  • The developed model offers a significant improvement for short-term water demand forecasting.
  • This methodology can be integrated into existing WDF systems, particularly in data preprocessing.
  • Optimized forecasting enhances operational efficiency in water supply management.