Improving Dry Season Forecasting in Indonesia Using Neural Network: Enhancing Drought, Water Scarcity, and Agricultural Impact Predictions
Keywords:
Neural Networks, Dry Season Forecasting, Drought Prediction, Water Scarcity, Agricultural ImpactAbstract
Indonesia, a tropical country with diverse geographical and climatic conditions, faces significant challenges during its dry season, which occurs from May to October. This period is often marked by droughts, water scarcity, and agricultural disruptions, all of which have substantial economic and social consequences. Despite the importance of accurate dry season forecasts, existing prediction methods, primarily based on traditional statistical models, struggle to provide reliable and precise forecasts, especially given Indonesia's complex climate. This research investigates the application of neural network-based models to improve the forecasting of dry season events, such as droughts and water shortages, in Indonesia. Neural networks are capable of handling large, non-linear datasets and can adapt to changing patterns in climate data. The model developed in this study utilizes meteorological data, including temperature, humidity, and precipitation, to predict the timing, intensity, and regional variations of dry season events. The results indicate that the neural network model significantly outperforms traditional forecasting methods in terms of accuracy and reliability. It provides more precise predictions of drought occurrences, water scarcity, and their impact on agriculture. These improved forecasts are crucial for enabling more effective decision-making in agriculture, water resource management, and disaster preparedness. This research highlights the potential of machine learning techniques in enhancing climate prediction and addressing the challenges of climate variability in Indonesia. By improving the accuracy of dry season forecasts, this study contributes to better risk management, more sustainable agricultural practices, and enhanced resilience to climate change, ultimately supporting Indonesia's long-term environmental and economic stability.
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