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Improving Flood Prediction: Assimilating Uncertain Crowdsourced Data Into Hydrological Models

Troy Simmons

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Improving Flood Prediction Assimilating Uncertain Crowdsourced Data into Hydrologic and Hydraulic Models (IHE Delft PhD Thesis Series)

by Jason Reza Jorjani

4.4 out of 5

Language	:	English
File size	:	31395 KB
Screen Reader	:	Supported
Print length	:	240 pages

Floods are one of the most devastating natural hazards, causing widespread damage and loss of life. Accurate flood prediction is crucial for reducing the impacts of these events and saving lives. Traditional flood prediction methods rely on data from gauges and weather stations, which can be sparse and unreliable in many areas.

Crowdsourced data, such as reports from citizens and social media posts, can provide valuable information about flooding in real time. However, these data are often uncertain and incomplete, making it difficult to incorporate them into hydrological models.

This book presents groundbreaking research on the assimilation of uncertain crowdsourced data into hydrological models for improved flood prediction. The book covers the latest advancements in data collection, uncertainty quantification, and data assimilation techniques.

Data Collection

The first step in improving flood prediction is to collect data from a variety of sources. Traditional data sources, such as gauges and weather stations, provide valuable information about the state of the hydrological system. However, these data are often sparse and unreliable in many areas.

Crowdsourced data can provide valuable information about flooding in real time. Citizens can report flooding using mobile apps or social media, and these reports can be used to supplement traditional data sources. However, crowdsourced data are often uncertain and incomplete, making it difficult to incorporate them into hydrological models.

This book presents innovative techniques for collecting crowdsourced data about flooding. The book covers the latest advancements in mobile app development, social media mining, and data quality control.

Uncertainty Quantification

Uncertainty is a major challenge in flood prediction. Traditional flood prediction methods often ignore uncertainty, which can lead to inaccurate predictions. This book presents groundbreaking research on uncertainty quantification in flood prediction.

The book covers the latest advancements in uncertainty quantification techniques, such as Bayesian inference and ensemble forecasting. These techniques can be used to quantify the uncertainty in crowdsourced data and hydrological models.

Data Assimilation

Data assimilation is the process of combining data from different sources to improve the accuracy of a model. This book presents groundbreaking research on data assimilation techniques for flood prediction.

The book covers the latest advancements in data assimilation techniques, such as Kalman filtering and particle filtering. These techniques can be used to assimilate crowdsourced data into hydrological models and improve the accuracy of flood predictions.

Applications

The techniques presented in this book can be used to improve flood prediction in a variety of applications. These applications include:

Real-time flood forecasting
Early warning systems
Flood risk assessment
Floodplain management

This book provides groundbreaking insights into the innovative techniques for improving flood prediction by incorporating uncertain crowdsourced data into hydrological models. The book covers the latest advancements in data collection, uncertainty quantification, and data assimilation techniques. These techniques can be used to develop real-time flood forecasting and early warning systems that can save lives and reduce the impacts of flooding.

I encourage you to download this book and learn more about the latest advancements in flood prediction. This book is a must-read for anyone interested in improving flood prediction and reducing the impacts of flooding.

Download the Book

Improving Flood Prediction Assimilating Uncertain Crowdsourced Data into Hydrologic and Hydraulic Models (IHE Delft PhD Thesis Series)

by Jason Reza Jorjani

4.4 out of 5