Date of Award

2021-05-01

Degree Name

Master of Science

Department

Civil Engineering

Advisor(s)

Vivek Tandon

Abstract

Machine learning technologies have helped provide answers for problems with a high degree of complexity. Machine learning has been utilized by various disciplines within the Civil Engineering profession and has proven to be efficient in solving complex problems. Although machine learning is being used in the Civil Engineering profession, a formal framework on developing and integrating machine learning has not been developed for flood depth prediction. The proposed word uses machine learning to predict the depth of flood at Houston, TX, due to a 100-year 24-hour storm. The proposed work can be used to collect, store and analyze data to solve for flood depth of a region. The machine learning application will identify a problem encountered in determining flood depth in areas of flooding. The machine learning application will also delineate the steps in collecting data and integrating the problem and data into machine learning. An optimization protocol is established to enhance the machine learning predictions by changing the training size. The training size was varied from 10% percent to 100%. The machine learning work reported the predicted values for flood depth, the mean squared error for the prediction, and the normalized values between the prediction and the actual values. Moreover, GIS software is used to visualize the effect of the prediction capabilities of machine learning as the training size is increased. The spatial visualization helps to understand how the prediction is compared with the actual dataset when mapped in the study region.

Language

en

Provenance

Received from ProQuest

File Size

391 p.

File Format

application/pdf

Rights Holder

Armando Esquivel

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