Flood Forecasting Using Machine Learning Methods
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Flood Forecasting Using Machine Learning Methods
Nowadays, the degree and scale of flood hazards has been massively increasing as a result of the changing climate, and large-scale floods jeopardize lives and properties, causing great economic losses, in the inundation-prone areas of the world. Early flood warning systems are promising countermeasures against flood hazards and losses. A collaborative assessment according to multiple disciplines, comprising hydrology, remote sensing, and meteorology, of the magnitude and impacts of flood hazards on inundation areas significantly contributes to model the integrity and precision of flood forecasting. Methodologically oriented countermeasures against flood hazards may involve the forecasting of reservoir inflows, river flows, tropical cyclone tracks, and flooding at different lead times and/or scales. Analyses of impacts, risks, uncertainty, resilience, and scenarios coupled with policy-oriented suggestions will give information for flood hazard mitigation. Emerging advances in computing technologies coupled with big-data mining have boosted data-driven applications, among which Machine Learning technology, with its flexibility and scalability in pattern extraction, has modernized not only scientific thinking but also predictive applications. This book explores recent Machine Learning advances on flood forecast and management in a timely manner and presents interdisciplinary approaches to modelling the complexity of flood hazards-related issues, with contributions to integrative solutions from a local, regional or global perspective.
Flood Forecasting Using Machine Learning Methods
This book is a printed edition of the Special Issue Flood Forecasting Using Machine Learning Methods that was published in Water.
Development of Flood Prediction Models Using Machine Learning Techniques
"Flooding and flash flooding events damage infrastructure elements and pose a significant threat to the safety of the people residing in susceptible regions. There are some methods that government authorities rely on to assist in predicting these events in advance to provide warning, but such methodologies have not kept pace with modern machine learning. To leverage these algorithms, new models must be developed to efficiently capture the relationships among the variables that influence these events in a given region. These models can be used by emergency management personnel to develop more robust flood management plans for susceptible areas. The research investigates machine learning techniques to analyze the relationships between multiple variables influencing flood activities in Missouri. The first research contribution utilizes a deep learning algorithm to improve the accuracy and timelessness of flash flood predictions in Greene County, Missouri. In addition, a risk analysis study is conducted to advise the existing flash flood management strategies for the region. The second contribution presents a comparative analysis of different machine learning techniques to develop a classification model and predict the likelihood of flash flooding in Missouri. The third contribution introduces an ensemble of Long Short-Term Memory (LSTM) deep learning models used in conjunction with clustering to create virtual gauges and predict river water levels at unmonitored locations. The LSTM models predict river water levels 4 hours in advance. These outputs empower emergency management decision makers with an advanced warning to better implement flood management plans in regions of Missouri not served with river gauge monitoring"--Abstract, page iv.