A Long Short-Term Memory-Based Prototype Model for Drought Prediction

This study presents the development of a deep learning model to predict droughts in the coastal region of Ecuador. Historical information from local meteorological stations was used, including data on precipitation, temperature, humidity, evapotranspiration, and soil moisture. A multi-layered artificial neural network was used. It was trained and evaluated by cross-validation, comparing it with other machine learning algorithms. The results demonstrate that the proposed model achieved a remarkable accuracy of 98.5% and a high sensitivity of 97.2% in predicting drought events in the coastal region of Ecuador. This exceptional performance underscores the model's potential for effective decision making to prevent and mitigate droughts. In addition, the study's limitations are discussed, and possible improvements are proposed, such as the incorporation of satellite data and the analysis of other environmental variables. This study highlights the importance of deep learning models in drought prediction and their potential to contribute to sustainable management in areas vulnerable to this climatic phenomenon.