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PREDICTING DEBRIS YIELD
USING ARTIFICIAL INTELLIGENCE MODELS
by
Zhiqing Kou
A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(CIVIL ENGINEERING)
August 2010
Copyright 2010 Zhiqing Kou
Object Description
| Title | Predicting debris yield using artificial intelligence models |
| Author | Kou, Zhiqing |
| Author email | zkou@usc.edu; kouzhiqing@yahoo.com.cn |
| Degree | Doctor of Philosophy |
| Document type | Dissertation |
| Degree program | Civil Engineering |
| School | Viterbi School of Engineering |
| Date defended/completed | 2009-10-15 |
| Date submitted | 2010 |
| Restricted until | Unrestricted |
| Date published | 2010-08-05 |
| Advisor (committee chair) | Lee, Jiin-Jen |
| Advisor (committee member) |
Wellford, L. Carter Nasseri, Iraj Lee, Vincent W. Moore, James E., II |
| Abstract | Artificial Neural Network is a very powerful computational tool for modeling very complicated and highly nonlinear problems in various fields. In this study, it is first applied to estimate accumulated debris yield in 14 debris basins within Los Angeles County, California as a result of a series of storm events from watersheds partially or totally burned by wildfires from 1984 to 2003. ANN models achieve very satisfactory modeling results as compared to a statistical model.; The ANN technique is then applied to forecast unit debris yield collected from 36 small debris basins within the county resulting from single significant storm events from 1938 to 1983. The same unit debris yield data is simulated by another two artificial intelligence models, Adaptive-Network-Based Fuzzy Inference System (ANFIS) and Generalized Dynamic Fuzzy Neural Network (GD-FNN) model. In addition to four basic input parameters: drainage area, watershed relief ratio, maximum one-hour rainfall intensity, and fire factor, six watershed morphological parameters such as elongation ratio, drainage density, hypsometric index, total stream length, mean bifurcation ratio, and transport efficiency factor are included as input parameters and their relative importance are determined through sensitivity analysis.; ANN models are also developed for modeling unit debris yield at 80 small debris basins. They are classified into five groups based on the relief ratios of their upstream watersheds: mild slope, steep slope, steeper slope, extreme steep slope, and the steepest slope. In addition to four aforementioned basic input parameters, three soil properties including soil erodibility factor, permeability rate, and liquid limit are considered as input parameter one by one to study their impact on the simulation.; Unit debris yield collected from large watersheds with area between 10 and 25 mi2, between 25 and 50 mi2, and between 50 and 200 mi2 are also simulated by neural network models. The modeling results indicate that the accuracy of unit debris yield estimated by ANN models is significantly higher than those obtained from ANFIS, GD-FNN model, and empirical equations developed by US Army Corps of Engineers. |
| Keyword | debris yield; sediment yield; artificial intelligence model; artificial neural network; adaptive-network-based fuzzy inference system; generalized dynamic fuzzy neural network |
| Geographic subject (county) | Los Angeles |
| Geographic subject (state) | California |
| Coverage date | 1938/2003 |
| Language | English |
| Part of collection | University of Southern California dissertations and theses |
| Publisher (of the original version) | University of Southern California |
| Place of publication (of the original version) | Los Angeles, California |
| Publisher (of the digital version) | University of Southern California. Libraries |
| Provenance | Electronically uploaded by the author |
| Type | texts |
| Legacy record ID | usctheses-m3295 |
| Rights | Kou, Zhiqing |
| Repository name | Libraries, University of Southern California |
| Repository address | Los Angeles, California |
| Repository email | http://www.usc.edu/isd/libraries/services/ask_a_librarian/email/ |
| Filename | etd-Kou-3839 |
| Archival file | uscthesesreloadpub_Volume40/etd-Kou-3839.pdf |
Description
| Title | Page 1 |
| Full text | PREDICTING DEBRIS YIELD USING ARTIFICIAL INTELLIGENCE MODELS by Zhiqing Kou A Dissertation Presented to the FACULTY OF THE USC GRADUATE SCHOOL UNIVERSITY OF SOUTHERN CALIFORNIA In Partial Fulfillment of the Requirements for the Degree DOCTOR OF PHILOSOPHY (CIVIL ENGINEERING) August 2010 Copyright 2010 Zhiqing Kou |
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