Enhancing Flood Monitoring and Prevention Using Machine Learning and IoT Integration

Abstract

Floods require good monitoring and preventive measures; they pose risk factors to humankind, property, and infrastructure. This thesis proposes a new approach to enhancing the integration of Internet of Things (IoT) infrastructure through utilizing Machine Learning (ML) methods for improved flood management and prevention. The system comprises three stations, namely: the water station, repeater station, and siren station. The water station is implanted with a radar sensor to monitor the water level continuously. Repeater stations actuate smooth communication and data flow by transmitting data between the stations. The siren station is anchored with a suite of environmental sensors that includes wind speed, wind direction, humidity, air pressure, atmospheric temperature, and rain gauges to present an overall view conducive to flood events. It is the data from this gathered sensor that becomes the basis for machine learning data set development. Then, one scenario is tested to determine how predictive the suggested method would be. Rainfall is the output variable in the scenario, which holds wind speed, wind direction, humidity, atmospheric pressure, atmospheric temperature, humidity, and water level as input features. In this dataset, preprocessing techniques are applied to remove the outliers, noise level, and missing values, assured with analysis at every step of the accuracy and reliability of the input data for further research on the study. The collected sensor data is utilized to predict flood episodes with the help of machine learning models, including 1D Convolutional Neural Networks (CNN) and Multivariate Long Short-Term Memory (LSTM) networks. The 1D-CNN models include the spatial relationships among the input characteristics in the case of the Multivariate LSTM models; it makes use of its capacity to capture the temporal dependencies in multivariate time series data. The models were evaluated through standard measures, such as Mean Square Error (MSE), and informed about their generalization and prediction accuracy. iv The implications are further important for flood monitoring and prevention efforts. The combination of IoT technology and machine learning methodologies will enable the authorities to preempt better and prevent the incidence of floods. The combined approach of environmental and radar sensors offers the most comprehensive approach to flood monitoring, designed to consider both meteorological and hydrological parameters. Future lines of research can include the development of flood management and catastrophe response decision support systems, the investigation of highly advanced machine learning algorithms, and the incorporation of increased sensor data.