Data Driven Approaches for Water Level Prediction

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dc.contributor.advisorEick, Christoph F.
dc.contributor.committeeMemberLaszka, Aron
dc.contributor.committeeMemberYuan, Xiaojing
dc.creatorNemilidinne, Anusha 1992-
dc.date.accessioned2019-11-07T04:07:55Z
dc.date.createdAugust 2019
dc.date.issued2019-08
dc.date.submittedAugust 2019
dc.date.updated2019-11-07T04:07:55Z
dc.description.abstractFloods are a hazardous disaster which threaten the United States and its territories as they affect millions of people every year. In this context, having suitable approaches for forecasting flooding will benefit people by reducing property damage and saving lives, by warning citizens of dangerous flooding events in advance. Consequently, many cities have developed sensor-based early warning systems which report water levels in real-time. The goal of this thesis is to take advantage of the data collected by such systems to develop data-driven water level prediction techniques. In past research, physics-based hydrology models have been developed, such as the National Water Model, which predict water levels by simulating the rise and fall of water. The purpose of this research is to generate alternative, complementary, data-driven water-level forecasting models using existing statistical models and recurrent neural networks which extrapolate the past into the future. We investigate various time series forecasting approaches, in particular: Vector Autoregressive (VAR) and Long Short-Term Memory (LSTM) Networks. The investigated forecasting techniques are applied and evaluated using USGS datasets. Moreover, we analyze the role of soil moisture — a less explored parameter — in flood incidence and conduct some experiments that explore the relationship between rainfall and soil moisture. Finally, we develop a web application called FloodNet, a real time water level prediction system, with a forecast horizon of 2 hours for a location along Buffalo Bayou in Houston, Texas.
dc.description.departmentComputer Science, Department of
dc.format.digitalOriginborn digital
dc.format.mimetypeapplication/pdf
dc.identifier.urihttps://hdl.handle.net/10657/5314
dc.language.isoeng
dc.rightsThe author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s).
dc.subjectFlood forecasting
dc.subjectDeep learning
dc.subjectLSTM
dc.subjectBuffalo Bayou
dc.titleData Driven Approaches for Water Level Prediction
dc.type.dcmiText
dc.type.genreThesis
local.embargo.lift2021-08-01
local.embargo.terms2021-08-01
thesis.degree.collegeCollege of Natural Sciences and Mathematics
thesis.degree.departmentComputer Science
thesis.degree.disciplineComputer Science
thesis.degree.grantorUniversity of Houston
thesis.degree.levelMasters
thesis.degree.nameMaster of Science

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