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Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM

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High-Resolution Rainfall Estimation Using Ensemble Learning Techniques and Multisensor Data Integration.

Maulana Putra¹, Mohammad Syamsu Rosid¹, Djati Handoko¹

¹Department of Physics, FMIPA Universitas Indonesia, Depok 16424, Indonesia.

Sensors (Basel, Switzerland)

|August 10, 2024

Summary

This summary is machine-generated.

A new rainfall estimation model integrates rain gauges, weather radars, and satellites for Indonesia. This extreme gradient boosting (XGBoost) model improves accuracy for diverse rainfall patterns, crucial for effective monitoring.

Keywords:

ensemble learning multisensor rainfall

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Area of Science:

Hydrology and Meteorology
Data Science and Machine Learning
Remote Sensing

Background:

Indonesia's complex rainfall patterns necessitate high-resolution, wide-coverage estimation systems.
Existing monitoring methods face challenges due to sparse rain gauge data, limited radar coverage, and data imbalance.

Purpose of the Study:

To develop an integrated rainfall estimation model for Indonesia.
To enhance rainfall monitoring capabilities across diverse spatial and temporal scales.

Main Methods:

Integrated data from rain gauges, weather radars, and weather satellites.
Applied extreme gradient boosting (XGBoost) ensemble learning technique.
Incorporated bias correction for satellite data and combined multiple weather radar inputs.

Main Results:

Achieved high estimation accuracy across six validation points (0.89-0.92).
Demonstrated low Root Mean Square Error (RMSE) values (1.85-3.08 mm/h).
Successfully captured various Indonesian rainfall patterns (seasonal, equatorial, local) with near real-time, high temporal resolution.

Conclusions:

The developed model shows significant potential for accurate rainfall estimation in Indonesia.
The integration of multiple data sources and XGBoost offers a robust solution for complex rainfall monitoring.
This approach provides valuable insights for hydrological and meteorological applications at high spatial and temporal resolutions.