What does this research mean for the field?

A multi-source satellite-driven machine learning framework utilizing XGBoost accurately predicts precipitation in data-poor, semi-arid regions, identifying soil wetness, land surface temperature, and atmospheric moisture as the primary drivers of precipitation variability. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

To develop and evaluate a multi-source, satellite-driven machine learning framework for precipitation prediction and to identify key climatic drivers in data-scarce semi-arid environments.

September 14, 2026Discover EnvironmentOpen Access

Machine learning based precipitation modeling using multi satellite data for climate resilient water resource management in Bundelkhand India

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Authors

PKPavan Kumar MPMegha Paul PSPrashant K. Srivastava

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Overview

Comparative modeling study demonstrates superior rainfall prediction using extreme gradient boosting over neural networks, highlighting viable climate tools for data-scarce regions.

Key Points

To develop and evaluate a multi-source, satellite-driven machine learning framework for precipitation prediction and to identify key climatic drivers in data-scarce semi-arid environments.
Trained Convolutional Neural Networks (CNN) and Extreme Gradient Boosting (XGBoost) algorithms using satellite-derived inputs, including land surface temperature, atmospheric moisture, surface pressure, wind speed, relative humidity, and soil wetness.
Evaluated model accuracy using standard statistical error metrics and assessed predictor importance via interpretable machine learning techniques.
XGBoost demonstrated superior predictive accuracy over CNN, achieving R2 = 0.77, RMSE = 88.79, and MAE = 42.06, compared to R2 = 0.60, RMSE = 120.82, and MAE = 73.46 for CNN.
Interpretability analysis revealed that soil wetness, land surface temperature, and atmospheric moisture serve as the primary variables controlling precipitation variability.

Cite This Study

Kumar et al. (2026) studied this question.

synapsesocial.com/papers/6aa7b26d0926e14a848b0d76 https://doi.org/10.1007/s44274-026-01047-x

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September 14, 2026Discover EnvironmentOpen Access

Machine learning based precipitation modeling using multi satellite data for climate resilient water resource management in Bundelkhand India

View Full Paper

Ask AI

Bookmark

Authors

PKPavan Kumar MPMegha Paul PSPrashant K. Srivastava

Discussion

Member takes

Overview

Comparative modeling study demonstrates superior rainfall prediction using extreme gradient boosting over neural networks, highlighting viable climate tools for data-scarce regions.

Key Points

To develop and evaluate a multi-source, satellite-driven machine learning framework for precipitation prediction and to identify key climatic drivers in data-scarce semi-arid environments.
Trained Convolutional Neural Networks (CNN) and Extreme Gradient Boosting (XGBoost) algorithms using satellite-derived inputs, including land surface temperature, atmospheric moisture, surface pressure, wind speed, relative humidity, and soil wetness.
Evaluated model accuracy using standard statistical error metrics and assessed predictor importance via interpretable machine learning techniques.
XGBoost demonstrated superior predictive accuracy over CNN, achieving R2 = 0.77, RMSE = 88.79, and MAE = 42.06, compared to R2 = 0.60, RMSE = 120.82, and MAE = 73.46 for CNN.
Interpretability analysis revealed that soil wetness, land surface temperature, and atmospheric moisture serve as the primary variables controlling precipitation variability.

Cite This Study

Kumar et al. (2026) studied this question.

synapsesocial.com/papers/6aa7b26d0926e14a848b0d76 https://doi.org/10.1007/s44274-026-01047-x

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