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Related Experiment Video

Updated: Jun 14, 2025

Surface Renewal: An Advanced Micrometeorological Method for Measuring and Processing Field-Scale Energy Flux Density Data

Surface Renewal: An Advanced Micrometeorological Method for Measuring and Processing Field-Scale Energy Flux Density Data

Published on: December 12, 2013

Federated learning based reference evapotranspiration estimation for distributed crop fields.

Muhammad Tausif¹, Muhammad Waseem Iqbal¹, Rab Nawaz Bashir^2,3

¹Department of Computer Science & Information Technology, The Superior University, Lahore, Punjab, Pakistan.

|February 5, 2025

Summary

This summary is machine-generated.

Federated learning enhances Reference Evapotranspiration (ETo) estimation across diverse locations. The Random Forest Regressor model achieved superior accuracy, outperforming local models for sustainable agriculture and water resource management.

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Area of Science:

Environmental Science
Agricultural Science
Data Science

Background:

Accurate Reference Evapotranspiration (ETo) is crucial for water resource management and sustainable agriculture.
Existing machine learning models for ETo estimation are often limited to specific geographical areas.
Centralized data approaches face challenges with privacy and data transfer limitations.

Purpose of the Study:

To propose a federated learning approach for estimating ETo across multiple locations with diverse weather conditions.
To evaluate the performance of different machine learning models (RFR, SVR, DTR) in a federated learning framework for ETo prediction.
To identify the key weather parameters influencing ETo estimation through feature importance analysis.

Main Methods:

Implemented a federated learning approach using weather data from three distinct geographical locations in Pakistan (2012-2022).
Trained and evaluated Random Forest Regressor (RFR), Support Vector Regressor (SVR), and Decision Tree Regressor (DTR) models locally and globally.
Conducted feature importance analysis to understand the impact of weather parameters on model performance.

Main Results:

The federated learning model based on Random Forest Regressor (RFR) demonstrated superior performance, achieving R2 = 0.97%, RMSE = 0.44, MAE = 0.33 mm day-1, and MAPE = 8.18%.
The RFR federated model outperformed individual local machine learning models at each selected site.
Maximum temperature and wind speed were identified as the most influential factors for ETo prediction.

Conclusions:

Federated learning offers a generalized and privacy-preserving method for accurate ETo estimation across varied climatic conditions.
The RFR model within a federated learning framework is highly effective for regional ETo prediction.
Understanding feature importance aids in refining ETo estimation models for better water resource and agricultural management.