Evaluating Levee Failure Susceptibility on the Mississippi River Using Logistic Regression Analysis and GPS Surveying

Author

Andrew Douglas Flor, Southern Illinois University CarbondaleFollow

Date of Award

1-1-2009

Degree Name

Master of Science

Department

Geology

First Advisor

Pinter, Nicholas

Abstract

This study utilized a new database of levee failures along 685 km of the Middle and Lower Mississippi River from St. Louis, MO to Memphis, TN during the past 120 years. The goals of this investigation were to: 1) identify the relative importance of geologic and geomorphic factors that have led to levee failures through the past century along the Mississippi River and 2) measure levee crest elevations to determine if they have increased or decreased between 1998 and 2007 and if they are built to the proper design grade elevation. Logistic regression analysis was utilized to examine selected site characteristics at each levee failure location. These site characteristics (levee failure parameters) included: 1) levee underlain by previous channel fill, 2) presence/absence of borrow pit, 3) location of failure on a meander bend, 4) width of channel, 5) width of floodway, 6) constriction-over-time factor, 7) land-cover type, 8) width of vegetative buffer, 9) sinuosity of channel, 10) intensity of dredging, and 11) presence/absence of bank revetment. Each of these parameters was evaluated using geologic maps, soil survey data, Digital Elevation Models (DEMs), historic river maps, and dredging reports (Winkley, 1977; Pinter et al., 2004). Two models were created for each river reach. The first model for both reaches used a 95% significance threshold, while the second model for the MMR used a 80% significance threshold, and the second model for the LMR used a 90% significance threshold. The first model for the Middle Mississippi River (MMR) identified only the presence/absence of channel fills to predict levee failure as significant, had an R² value of 0.178, a p-value of 0.002, and a percentage accuracy of 68.6%. The second model for the MMR identified the following variables as significant: presence/absence of channel fills to predict levee failure, location of failure on a meander bend, channel width, land-cover type, and intensity of dredging. This model had an R2 value of 0.408, p-value of 0.002, and a percentage accuracy of 74.3%. The 95% model for the Lower Mississippi River (LMR) identified location of failure on a meander bend, land-cover type, constriction-over-time factor, and sinuosity of the channel as significant. This model had an R2 value of 0.326, p-value of 0.003, and a percentage accuracy of 69.5%. The 90% LMR model identified the following variables as significant: the presence/absence of borrow pits, location of failure on meander bend, channel width, land-cover type, constriction-over-time factor, vegetative buffer width, channel sinuosity, and presence/absence of bank revetment. This model had an R2 value of 0.385, p-value of 0.006, and a percentage accuracy of 72.0%. The MMR and LMR models with the 95% significance threshold had no predictors in common because of differences between the two river reaches or possibly because of the small sample size. However, the expanded MMR and LMR models shared three predictors (i.e., meander location, channel width, and land cover type). The second portion of this project used post-processed dual-frequency GPS surveying to measure levee elevations between St. Louis, MO and Cairo, IL. These elevations were compared to the 50-year design flood grade elevations and to a 1998 DEM to identify areas of levee heightening, levee degradation and/or subsidence, and locations of past levee crevasses. This surveying revealed areas that, between 1998 and 2007, were raised up to ~1.5 meters and other areas that degraded or subsided up to ~1.0 meter. Also, the locations of five recent levee crevasses were investigated, showing local increases or decreases at those points. The importance of levee road construction type was identified through many sharp increases or decreases at the transition between road types. Overall, this project showed promising implications for the determination of levee failure susceptibility and proper levee elevation heights using logistic regression analysis and kinematic GPS surveying. The logistic regression models predicted the potential for levee failure based on local site characteristics of levees between St. Louis, MO and Memphis, TN. The high-precision kinematic GPS surveying illustrated levee elevations along the MMR to a high degree of accuracy, allowing for the rapid and efficient identification of areas that do not correspond to the proper design flood grade elevation.