Thesis Defense - University of Houston
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Thesis Defense

In Partial Fulfillment of the Requirements for the Degree of Master of Science

Christariny Hutapea

will defend her thesis

Computational Methods for Flood Forecasting


According to World Meteorological Organization (WMO), flood is one of the most hazardous natural disasters, affecting millions of people globally every year. Over the years, many research studies have been conducted with the objective of reducing the flood impacts to people’s life, 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 techn iques 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 for its users.

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 those approaches to different prediction scenarios including predicting water level 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 R-squared. The experiments showed mixed results for different scenarios, but, in general, the linear regression produced better results than the other approaches. However, because the RMSEs values are quite high, we have to look for a better approach.

Date: Thursday, July 14, 2016
Time: 11:00 AM
Place: PGH 550
Advisor: Dr. Christoph Eick

Faculty, students, and the general public are invited.