Computational Methods for Flood Forecasting --------------------------------------------- A Thesis Presented to the Faculty of the Department of Computer Science University of Houston --------------------------------------------- In Partial Fulfillment of the Requirements for the Degree Master of Science --------------------------------------------- By Christariny Hutapea August 2016 Computational Methods for Flood Forecasting Christariny Hutapea APPROVED: Dr. Christoph Eick, Chairman Department of Computer Science Dr. Weidong Shi Department of Computer Science Dr. Klaus Kaiser Department of Mathematics Dean, College of Natural Sciences and Mathematics ii Acknowledgements I would like to extend my gratitude to Dr. Christoph Eick, my advisor, for his continuous support and guidance during this endeavor. His valuable expertise, timely feedback and great patience have made it possible for me to complete this thesis. The unconditional love and prayers from my parents have also given me strength in this journey. Finally, this thesis is dedicated to my wonderful husband and daughter whose tremendous support and encouragement I am thankful for. iii Computational Methods for Flood Forecasting --------------------------------------------- An Abstract of a Thesis Presented to the Faculty of the Department of Computer Science University of Houston --------------------------------------------- In Partial Fulfillment of the Requirements for the Degree Master of Science --------------------------------------------- By Christariny Hutapea August 2016 iv 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 v scenarios was quite high with values greater than 0.5 feet; consequently, there is a need to look for better approaches. vi Contents 1 Introduction .............................................................................................................................. 1 2 Background and Survey on Recent Research on Flood-Related Problems.............................. 4 2.1 Background ...................................................................................................................... 4 2.2 Survey on Recent Research on Flood-Related Problems................................................. 7 2.2.1 Coastal Flood Risk Reduction Program .............................................................. 8 2.2.2 Houston-Galveston Area Protection System (H-GAPS) .................................... 8 2.2.3 Research by Department of Homeland Security ............................................... 11 3 Alleviating Flood Problems with Computational Method and Information Technology ...... 15 3.1 Flood Forecasting .......................................................................................................... 16 3.1.1 Storm Surge and Wave Computation Models .................................................. 16 3.1.2 Another Storm Surge Modelling and Wave Computation Models ................... 23 3.2 Flood Warning/Monitoring Systems.............................................................................. 26 3.2.1 Harris County Flood Warning System .............................................................. 26 3.2.2 The Rice University Flood Alert System .......................................................... 35 3.2.3 Central Texas HUB ........................................................................................... 38 3.2.4 Flood Warning/Monitoring Systems on Mobile Devices ................................. 41 3.3 Flood Response Management ........................................................................................ 44 4 Case Study: Water-Level Forecasting in Harris County ........................................................ 45 4.1 Objective ........................................................................................................................ 45 4.2 Datasets .......................................................................................................................... 46 4.2.1 Data Entities ...................................................................................................... 46 4.2.2 Extraction Tool: Selenium ................................................................................ 50 4.2.3 Data Extraction ................................................................................................. 52 4.2.4 Data Pre-Processing .......................................................................................... 54 4.3 Exploratory Data Analysis ............................................................................................. 55 4.3.1 Exploratory Data Analysis for Particular Locations ......................................... 56 4.3.2 Exploratory Data Analysis for the Addicks Reservoir Watershed ................... 59 4.3.3 Global Exploratory Data Analysis .................................................................... 65 4.3.4 Data Cleaning Procedure .................................................................................. 67 4.4 Forecasting Scenarios and Evaluation Measures ........................................................... 71 4.4.1 Forecasting Scenarios ....................................................................................... 71 4.4.2 Forecasting Methods ......................................................................................... 72 4.4.3 Forecasting Models ........................................................................................... 76 4.4.4 Evaluation Measures ......................................................................................... 79 4.5 Experimental Method .................................................................................................... 80 4.5.1 Location-Specific Forecasting Scenario ........................................................... 81 4.5.2 The Addicks Reservoir Watershed Water-Level Forecasting ........................... 84 4.5.3 Global Water-Level Forecasting ....................................................................... 87 vii 4.6 Experimental Results and Discussions .......................................................................... 87 4.6.1 Location-Specific Water-Level Forecasting Scenario ...................................... 87 4.6.2 Addicks Reservoir Watershed Water-Level Forecasting Scenario ................... 89 4.6.3 Global Water-Level Forecasting Scenario ........................................................ 93 5 Conclusion ............................................................................................................................. 96 viii List of Figures Figure 1. Operational SLOSH Model Coverage. ........................................................................... 19 Figure 2. Sample Output of the SLOSH Model for Hurricane Ike Displayed in SDP................... 20 Figure 3. Harris County Flood Warning Website Main Page. ....................................................... 29 Figure 4. View of Stream Elevation Data. ..................................................................................... 31 Figure 5. View of Rainfall Amount Data. ...................................................................................... 32 Figure 6. Harris County Flood Warning System High Level Architecture. .................................. 34 Figure 7. The Rice University Flood Alert System Website Main Page. ...................................... 37 Figure 8. Central Texas HUB Main Page. ..................................................................................... 39 Figure 9. Rivercast Application ..................................................................................................... 42 Figure