Browsing by Author "Mannens, Erik"

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Extreme Rainfall Event Classification Using Machine Learning for Kikuletwa River Floods
(MDPI, 2023-03-08) Mdegela, Lawrence; Municio, Esteban; Bock, Yorick; Luhanga, Edith; Leo, Judith; Mannens, Erik
Advancements in machine learning techniques, availability of more data sets, and increased computing power have enabled a significant growth in a number of research areas. Predicting, detecting, and classifying complex events in earth systems which by nature are difficult to model is one such area. In this work, we investigate the application of different machine learning techniques for detecting and classifying extreme rainfall events in a sub-catchment within the Pangani River Basin, found in Northern Tanzania. Identification and classification of extreme rainfall event is a preliminary crucial task towards success in predicting rainfall-induced river floods. To identify a rain condition in the selected sub-catchment, we use data from five weather stations that have been labeled for the whole sub-catchment. In order to assess which machine learning technique is better suited for rainfall classification, we apply five different algorithms in a historical dataset for the period of 1979 to 2014. We evaluate the performance of the models in terms of precision and recall, reporting random forest and XGBoost as having the best overall performances. However, because the class distribution is imbalanced, a generic multi-layer perceptron performs best when identifying heavy rainfall events, which are eventually the main cause of rainfall-induced river floods in the Pangani River Basin
Extreme Rainfall Events Classification Using Machine Learning for Kikuletwa River Floods
(Preprints, 2023-02-20) Mdegela, Lawrence; Municio, Esteban; Bock, Yorick; Mannens, Erik; Luhanga, Edith; Leo, Judith
Advancements in Machine Learning techniques, availability of more data-sets, and 1 increased computing power have enabled a significant growth in a number research areas. Predicting, 2 detecting and classifying complex events in earth systems which by nature are difficult to model 3 is one of such areas. In this work, we investigate the application of different machine learning 4 techniques for detecting and classifying extreme rainfall events in a sub-catchment within Pangani 5 River Basin, found in Northern Tanzania. Identification and classification of extreme rainfall event 6is a preliminary crucial task towards success in predicting rainfall-induced river floods. To identify 7 a rain condition in the selected sub-catchment, we use data from five weather stations which have 8 been labeled for the whole sub-catchment. In order to assess which Machine Learning technique 9 suits better for rainfall classification, we apply five different algorithms in a historical dataset for the 10 period of 1979 to 2014. We evaluate the performance of the models in terms of precision and recall, 11 reporting Random Forest and XGBoost as the ones with best overall performance. However, since the 12 class distribution is imbalanced, the generic Multi-layer Perceptron performs best when identifying 13 the heavy rainfall events, which are eventually the main cause of rainfall-induced river floods in the 14 Pangani River Basin.
Monitoring Kikuletwa river levels in northern Tanzania: A data set unlocking insights for effective flood early warning systems
(Elsevier, 2023-08) Mdegela, Lawrence; Bock, Yorick; Luhanga, Edith; Leo, Judith; Mannens, Erik
Floods are a recurring natural disaster that pose significant risks to communities and infrastructure. The lack of reliable and accurate data on river systems in developing countries has hindered the development of effective flood early warning systems. This paper presents a data set collected using ultrasonic distance sensors installed at two locations along the Kikuletwa River in the Pangani River Basin, Northern Tanzania. The dataset consists of hourly measurements of river water levels, providing a high-resolution time series that can be used to study trends in water level changes and to develop more accurate flood early warning systems. The Kikuletwa River dataset has significant potential applications for flood management, including the calibration and validation of hydrological models, the identification of critical thresholds for flood warning, and the evaluation of flood forecasting techniques. The dataset can also be used to study the hydrological processes in the basin, such as the relationship between rainfall and river discharge, and to develop more efficient and effective flood management strategies. The ultrasonic distance sensors were configured to record river level data at hourly intervals, providing a continuous time series of river levels. The data was subjected to quality control procedures to ensure accuracy and consistency, and missing or erroneous data was corrected or removed where necessary.
A Multi-Modal Wireless Sensor System for River Monitoring: A Case for Kikuletwa River Floods in Tanzania
(MDPI, 2023-04-17) Mdegela, Lawrence; Bock, Yorick; Municio, Esteban; Luhanga, Edith; Leo, Judith; Mannens, Erik
Reliable 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.