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Random Forest and Multilayer Perceptron hybrid models integrated with the genetic algorithm for predicting pan evaporation of target site using a limited set of neighboring reference station data

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Abstract

This study explores the application of machine learning algorithms for the prediction of pan evaporation (Ep), which is a critical factor in water resource management for the assessment of water demand and usage. Specifically, this research evaluates the effectiveness of two base models: Random Forest (RF) and Multi-Layer Perceptron (MLP) and their optimized counterparts using a Genetic Algorithm (GA), designated as GA-RF and GA-MLP, for modeling Ep at a target station using data from adjacent stations. The datasets were split into a training set (70%) and a testing set (30%). The models’ performances were judged using three statistical measures: Correlation Coefficient (CC), Scattered Index (SI), and Willmott’s Index of agreement (WI). The enhanced models, particularly GA-MLP-5, showed superior performance with a CC of 0.8704, SI of 0.2539, and WI of 0.9212, indicating the potent ability of GA to refine RF and MLP models for predictive accuracy. Additionally, sensitivity analysis via the GA-RF indicates the varying influence of Ep from neighboring stations on the target station, shedding light on key predictors for effective water management. Conclusively, this study demonstrates that the hybrid models have significant potential in accurate Ep estimation and can be expanded to predict other meteorological variables, offering valuable tools for water resource management strategies.

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Data availability

The authors confirm that the data supporting the findings of this study are available within the article and its Supplementary material. Raw data that support the findings of this study are available from the corresponding author, upon reasonable request.

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Authors and Affiliations

  1. Department of Water Engineering, Faculty of Agriculture, University of Tabriz, Tabriz, Iran

    Sadra Shadkani, Sajjad Hashemi & Amirreza Pak

  2. Department of Civil Engineering, Faculty of Civil Engineering, University of Tabriz, Tabriz, Iran

    Alireza Barzgari Lahijan

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  1. Sadra Shadkani
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  2. Sajjad Hashemi
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  3. Amirreza Pak
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  4. Alireza Barzgari Lahijan
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Contributions

-Conceptualization: Sadra Shadkani, Sajjad Hashemi-Data curation: Sadra Shadkani, Sajjad Hashemi-Formal analysis: Sadra Shadkani, Sajjad Hashemi-Investigation: Sadra Shadkani, Sajjad Hashemi, Amirreza Pak-Methodology: Sadra Shadkani, Sajjad Hashemi-Resources: Sadra Shadkani, Amirreza Pak-Software: Sadra Shadkani-Supervision: Sadra Shadkani-Validation: Sadra Shadkani, Sajjad Hashemi-Visualization: Amirreza Pak, Sajjad Hashemi-Writing - original draft: Sadra Shadkani, Sajjad Hashemi, Amirreza Pak, -Writing - review & editing: Sadra Shadkani, Sajjad Hashemi, Amirreza Pak, Alireza Barzgari Lahijan

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Correspondence to Sadra Shadkani.

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Communicated by: H. Babaie

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Shadkani, S., Hashemi, S., Pak, A. et al. Random Forest and Multilayer Perceptron hybrid models integrated with the genetic algorithm for predicting pan evaporation of target site using a limited set of neighboring reference station data. Earth Sci Inform 17, 1261–1280 (2024). https://doi.org/10.1007/s12145-024-01237-2

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