Deep Learning-based Flood Risk Prediction for Climate Resilience Planning in Malawi

Climate change resilience in Malawi faces an institutional gap because most institutions often fail to prioritize risk data when dealing with climate extremes such as floods. This unfortunate gap forces many Malawians to fend for themselves during times of climate extremes This situation is also heightened by a few studies that utilize Time Series Analysis (TSA) and Deep Learning Models (DLM) to predict climate extremes for decision-making processes. Therefore, this study focused on flood risk prediction and assessment in six selected districts of Malawi: Chikwawa, Blantyre, Phalombe, Zomba, Rumphi, and Karonga. Traditional Time Series Models (ARIMA) and Semantic Convolution Deep Learning Analysis were used for this purpose. Data were retrieved from the database of the US National Aeronautics and Space Administration (NASA). The results revealed frequent and significant precipitation peaks in Blantyre and Chikwawa, particularly during the rainy season, suggesting that the areas are at a higher risk of flooding, with a high probability of infrastructural damage and economic losses. Karonga and Phalombe revealed cyclical trends with prominent spikes in rainfall. In contrast, Rumphi and Zomba exhibit less pronounced trends, though there are still significant fluctuations in rainfall patterns, suggesting an increasing likelihood of flood risk in future climate extremes. This study situates its policy implications by emphasizing that residents, institutions, government, partners, and NGOs need to take a problem-focused approach towards climate resilience planning, including updating flood risk maps, designing flood protection infrastructure, and preparing emergency response plans tailored to the specific needs of each district in Malawi.