Computational Methods for Flood Forecasting
| dc.contributor.advisor | Eick, Christoph F. | |
| dc.contributor.committeeMember | Shi, Weidong | |
| dc.contributor.committeeMember | Kaiser, Klaus | |
| dc.creator | Hutapea, Christariny 1981- | |
| dc.date.accessioned | 2018-11-30T21:32:42Z | |
| dc.date.available | 2018-11-30T21:32:42Z | |
| dc.date.created | August 2016 | |
| dc.date.issued | 2016-08 | |
| dc.date.submitted | August 2016 | |
| dc.date.updated | 2018-11-30T21:32:42Z | |
| dc.description.abstract | According to World Meteorological Organization (WMO), flooding is one of the most hazardous natural disasters, affecting millions of people globally every year. Over the years, many research have been conducted with the objective of reducing the impacts of flooding on peoples’ lives, the environment, and economy. This thesis surveys computational methods for flood management covering flood forecasting, flood warning and monitoring, and flood-response management. Most existing flood-forecasting models employ simulation techniques that operate on complex physics and mathematical equations representing the dynamics of the atmosphere and of water flow. Moreover, there are web-based and mobile applications that collect flood-related data from sensors, and serve as flood monitoring and warning systems. Furthermore, the thesis investigates water-level forecasting techniques relying on a regression approach. The investigated forecasting techniques are applied and evaluated for Harris County Flood Warning System (HCFWS) datasets. The purpose of the case study is to generate alternative water-level forecasting models using existing statistical forecasting techniques, in contrast to existing simulation approaches. We investigated several forecasting approaches including Linear Regression, Vector Autoregressive (VAR), and Autoregressive Integrated Moving Average (ARIMA) model. We applied these approaches to different forecasting scenarios including predicting water levels in Harris County at a particular location and in the Addicks Reservoir watershed. We compared each model’s performance using two statistics: Root Mean Square Error (RMSE) and Coefficient of Determination, or also known as R2. The experiments showed mixed results for different scenarios, but, in general, the linear regression produced better results than other approaches. However, the RMSE for some forecasting scenarios was quite high with values greater than 0.5 feet; consequently, there is a need to look for better approaches. | |
| dc.description.department | Computer Science, Department of | |
| dc.format.digitalOrigin | born digital | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.uri | http://hdl.handle.net/10657/3569 | |
| dc.language.iso | eng | |
| dc.rights | The 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.subject | Flood forecasting | |
| dc.subject | Flood management | |
| dc.subject | Vector autoregressive (VAR) | |
| dc.subject | Vector autoregressive (VAR) | |
| dc.subject | Autoregressive Integrated Moving Average | |
| dc.subject | ARIMA Model | |
| dc.subject | Flood-related research | |
| dc.subject | Water-level forecasting | |
| dc.subject | Harris County | |
| dc.subject | Flood events | |
| dc.title | Computational Methods for Flood Forecasting | |
| dc.type.dcmi | Text | |
| dc.type.genre | Thesis | |
| thesis.degree.college | College of Natural Sciences and Mathematics | |
| thesis.degree.department | Computer Science, Department of | |
| thesis.degree.discipline | Computer Science | |
| thesis.degree.grantor | University of Houston | |
| thesis.degree.level | Masters | |
| thesis.degree.name | Master of Science |