Prediction of Monthly Flood Occurrences in Indonesia Based on Long Short Term Memory Analysis
DOI:
https://doi.org/10.33379/gtech.v7i4.3346Keywords:
Flood, LSTM, MAPE, Forecasting, SDGsAbstract
Floods are among the most common and dangerous natural disasters worldwide, leading to loss of life and economic instability. In Indonesia, floods have been the most frequently occurring natural disaster since 2009. The high frequency underscores the urgency of predicting the number of natural disaster events to assist the government and the public in taking appropriate mitigation measures, as well as contributing to the achievement of Sustainable Development Goal 15 regarding Terrestrial Ecosystems. The method used to predict the monthly occurrence of floods in Indonesia is Long Short Term Memory (LSTM). LSTM was chosen for its ability to process sequential data over a long period of time. Upon analysis, highly accurate forecasting results were obtained, with a Mean Absolute Percentage Error (MAPE) of 8.04%, a Root Mean Square Error (RMSE) of 5.991. The model is also proficient at estimating training data, with an value of 95.71%.
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