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dc.contributor.authorMdegela, Lawrence
dc.contributor.authorBock, Yorick
dc.contributor.authorMunicio, Esteban
dc.contributor.authorLuhanga, Edith
dc.contributor.authorLeo, Judith
dc.contributor.authorMannens, Erik
dc.date.accessioned2023-11-14T08:32:54Z
dc.date.available2023-11-14T08:32:54Z
dc.date.issued2023-04-17
dc.identifier.urihttps://doi.org/10.3390/s23084055
dc.identifier.urihttps://dspace.nm-aist.ac.tz/handle/20.500.12479/2427
dc.descriptionA research article was submitted to Sensors 2023, volume 23en_US
dc.description.abstractReliable and accurate flood prediction in poorly gauged basins is challenging due to data scarcity, especially in developing countries where many rivers remain insufficiently monitored. This hinders the design and development of advanced flood prediction models and early warning systems. This paper introduces a multi-modal, sensor-based, near-real-time river monitoring system that produces a multi-feature data set for the Kikuletwa River in Northern Tanzania, an area frequently affected by floods. The system improves upon existing literature by collecting six parameters relevant to weather and river flood detection: current hour rainfall (mm), previous hour rainfall (mm/h), previous day rainfall (mm/day), river level (cm), wind speed (km/h), and wind direction. These data complement the existing local weather station functionalities and can be used for river monitoring and ext reme weather prediction. Tanzanian river basins currently lack reliable mechanisms foraccurately establishing river thresholds for anomaly detection, which is essential for flood prediction models. The proposed monitoring system addresses this issue by gathering information about river depth levels and weather conditions at multiple locations. This broadens the ground truth of river characteristics, ultimately improving the accuracy of flood predictions. We provide details on the monitoring system used to gather the data, as well as report on the methodology and the nature of the data. The discussion then focuses on the relevance of the data set in the context of flood prediction,the most suitable AI/ML-based forecasting approaches, and highlights potential applications beyond flood warning systems.en_US
dc.language.isoenen_US
dc.publisherMDPIen_US
dc.subjectwireless sensorsen_US
dc.subjectmulti-featured dataen_US
dc.subjectmachine learningen_US
dc.subjectriver floodsen_US
dc.subjectflood detectionen_US
dc.titleA Multi-Modal Wireless Sensor System for River Monitoring: A Case for Kikuletwa River Floods in Tanzaniaen_US
dc.typeArticleen_US


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