Version 1
: Received: 28 January 2023 / Approved: 31 January 2023 / Online: 31 January 2023 (02:13:16 CET)
Version 2
: Received: 31 January 2023 / Approved: 1 February 2023 / Online: 1 February 2023 (03:54:50 CET)
Mdegela, L.; De Bock, Y.; Municio, E.; Luhanga, E.; Leo, J.; Mannens, E. A Multi-Modal Wireless Sensor System for River Monitoring: A Case for Kikuletwa River Floods in Tanzania. Sensors2023, 23, 4055.
Mdegela, L.; De Bock, Y.; Municio, E.; Luhanga, E.; Leo, J.; Mannens, E. A Multi-Modal Wireless Sensor System for River Monitoring: A Case for Kikuletwa River Floods in Tanzania. Sensors 2023, 23, 4055.
Mdegela, L.; De Bock, Y.; Municio, E.; Luhanga, E.; Leo, J.; Mannens, E. A Multi-Modal Wireless Sensor System for River Monitoring: A Case for Kikuletwa River Floods in Tanzania. Sensors2023, 23, 4055.
Mdegela, L.; De Bock, Y.; Municio, E.; Luhanga, E.; Leo, J.; Mannens, E. A Multi-Modal Wireless Sensor System for River Monitoring: A Case for Kikuletwa River Floods in Tanzania. Sensors 2023, 23, 4055.
Abstract
Reliable and accurate flood prediction is a challenging task in poorly gauged basins due to data scarcity. Data is an essential component of any AI/ML model today, and the performance of such models hugely depends on the availability of sufficient amount of trusted, representative data. However, unlike a few well-studied rivers, most of the rivers in developing countries are still insufficiently monitored, which significantly hinges the design and development of advanced flood prediction models and early warning systems. This paper presents a multi-modal, sensor-based and near-real time river monitoring system to produce a multi-feature data set for the Kikuletwa river in Northern Tanzania, an area that heavily suffers from frequent floods. Our deployed system, which gather information about river depth levels and weather at several locations, aims at widening the ground truth of the river characteristics and eventually improve 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. Finally, we present the relevance of the data set in the context of flood prediction, discussing the most suitable AI/ML-based forecasting approaches, while also highlighting some applications of the data set beyond flood warning systems.
Keywords
Sensors; data-set; Machine learning; river floods; river level
Subject
Computer Science and Mathematics, Information Systems
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
