TL;DR
A hybrid rainfall prediction framework which integrates LSTM network with an interval Type-2 fuzzy logic system for one day ahead rainfall prediction and results indicate that the integration of fuzzy layer improves the predictive accuracy.
Initial validation against BMKG observation data in South Tangerang showed a relatively low temperature deviation during the testing period, confirming that the proposed CNN-LSTM model has adequate potential to support short-term weather prediction systems in Banten Province.
Global climate change is making weather conditions in Indonesia, particularly in Palembang City, increasingly difficult to predict. The unexpected high-intensity rainfall frequently triggers hydrometeorological disasters, leading to threats to human safety and substantial economic losses. Consequently, reliable rainfal...
The XGBoost, LSTM and Hybrid LSTM-XGBoost models outperformed the baseline, achieving a Coefficient of Determination of approximately 0.61 compared with 0.52, and the hybrid model performed best overall, with an RMSE of 38.68 mm.
The proposed method performed better than the conventional LSTM algorithm in all forecasting scenarios and showed robust performance even at a 7-day forecasting lead time, showing promise for applications in short-range soil moisture prediction and environmental monitoring studies.
Experimental results reveal that the DL methods achieve significantly better forecasting of temperature as compared to the conventional ARIMA model, providing strong evidence of the value of combining DL methods for weather prediction.
A hybrid forecasting model that fuses eXtreme Gradient Boosting for spatial feature importance evaluation with Long Short-Term Memory (LSTM) networks for sequential load prediction is proposed that provides a robust tool for proactive nutrient runoff management in data-sparse agricultural contexts.
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