Development and Implementation of a Machine Learning-Based Flood Forecasting System in Kasese District, Uganda

This study aimed to develop a proof-of-concept prototype of a machine learning system to forecast and mitigate the effect of floods in Kasese District. The researchers used a participatory design science approach. The researchers conducted document reviews and brainstorming to obtain past climate data from the representatives of affected communities, the Makerere University Department of Meteorology, and the Uganda National Meteorological Authority. Qualitative data were transcribed from recordings of the brainstorming sessions and notes from literature. The data were then summarized in tables and analyzed using Visual Network Analysis (VNA) with Word Clouds and Gephi Open Source Software. We employed a combination of C++ programming, sensors wired to Arduino 2 and 3 Integrated Development Environment System to build the prototype. Two machine learning algorithms, including linear regression and K-nearest neighbours (KNN) were used to learn from collected hydrological data and make necessary predictions. Using sensors, we were able to read water levels, temperature, and humidity. The prototype successfully demonstrated the ability to send early-warning alerts to users, contributing to both theoretical advancements in disaster risk reduction and practical tools for mitigating flood-related losses in Uganda. The researchers recommend further study to validate the use of this system and evaluate its efficacy and predictive accuracy in averting floods in affected areas.