Doctoral Dissertations

Bhanu Kanwar

Keywords and Phrases

Deep Learning; Flash Floods; Flood Prediction; Machine Learning; Natural Hazards; Neural Networks

Abstract

"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.

Advisor(s)

Corns, Steven

Committee Member(s)

Long, Suzanna, 1961-
Raper, Stephen A.
Kwasa, Benjamin J.
Nadendla, V. Sriram Siddhardh

Department(s)

Engineering Management and Systems Engineering

Degree Name

Ph. D. in Engineering Management

Comments

The author thanks the MidAmerica Transportation Center (MATC) and the Missouri Department of Transportation (MoDOT) for providing funds for the research projects over the last few years (grant numbers TR202023 and TR202111).

Publisher

Missouri University of Science and Technology

Publication Date

Summer 2022

Journal article titles appearing in thesis/dissertation

  • Deep learning-based disaster management planning and risk analysis of flash flood-prone regions
  • Deep neural network classifier for flash flood susceptibility
  • Ensemble deep learning for predicting gauge height at unmonitored river locations

Pagination

xv, 97 pages

Note about bibliography

Includes bibliographic references.

Rights

© 2022 Bhanu Partap Singh Kanwar, All rights reserved.

Document Type

Dissertation - Open Access

File Type

text

Language

English

Thesis Number

T 12160

Electronic OCLC #

1344518723

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