| dc.description.abstract | Reliable and accurate flood prediction is a challenging task in poorly gauged basins due 1 to data scarcity. Data is an essential component of any AI/ML model today, and the performance 2 of such models hugely depends on the availability of sufficient amount of trusted, representative 3 data. However, unlike a few well-studied rivers, most of the rivers in developing countries are still 4 insufficiently monitored, which significantly hinges the design and development of advanced flood 5 prediction models and early warning systems. This paper presents a multi-modal, sensor-based and 6near-real time river monitoring system to produce a mul ti-feature data set for the Kikuletwa river in 7 Northern Tanzania, an area that heavily suffers from frequent floods. Our deployed system, which 8 gather information about river depth levels and weather at several locations, aims at widening the 9
ground truth of the river characteristics and eventually improve the accuracy of flood predictions. We 10 provide details on the monitoring system used to gather the data as well as report on the methodology 11 and the nature of the data. Finally, we present the relevance of the data set in the context of flood 12 prediction, discussing the most suitable AI/ML-based forecasting approaches, while also highlighting 13 some applications of the data set beyond flood warning systems. | en_US |
