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ARS Home » Midwest Area » Columbia, Missouri » Cropping Systems and Water Quality Research » Research » Publications at this Location » Publication #294259

Title: Performance of Geno-Fuzzy Model on rainfall-runoff predictions in claypan watersheds

item SENAVIRATNE, ANOMAA - University Of Missouri
item UDAWATTA, RANJITH - University Of Missouri
item Baffaut, Claire
item ANDERSON, STEPHEN - University Of Missouri

Submitted to: ASA-CSSA-SSSA Annual Meeting Abstracts
Publication Type: Abstract Only
Publication Acceptance Date: 6/26/2013
Publication Date: 11/3/2013
Citation: Senaviratne, A., Udawatta, R.P., Baffaut, C., Anderson, S.H. 2013. Performance of Geno-Fuzzy Model on rainfall-runoff predictions in claypan watersheds [abstract]. ASA-CSSA-SSSA Annual Meeting. 148-1.

Interpretive Summary:

Technical Abstract: Fuzzy logic provides a relatively simple approach to simulate complex hydrological systems while accounting for the uncertainty of environmental variables. The objective of this study was to develop a fuzzy inference system (FIS) with genetic algorithm (GA) optimization for membership functions (MFs) for event-based rainfall-runoff prediction of three small adjacent row crop watersheds (1.65 to 4.44 ha) with intermittent discharge in the claypan soils of North East Missouri, prior to and after the establishment of upland contour grass and agroforestry (tree+grass) buffers. A Mamdani type FIS with five MFs and five fuzzy rules was created using MATLAB 7.10.0. Two sets of MFs were developed and optimized using GA for pre- and post-buffer conditions using one of the three watersheds. They were then validated using either another watershed or a different time period. The FIS simulated event-based runoff with r2 and Nash-Sutcliffe Coefficient (NSC) values greater than 0.65 for calibration and validation. The pre-buffer FIS simulated event-based runoff of two larger similar watersheds (140 ha and 259 ha) with r2 values of 0.82 and 0.68 and NSC values of 0.77 and 0.53, respectively. The GA optimization of MFs moderately improved r2 and NSC values. These FIS predictions of event-based runoff were similar to those of the Agricultural Policy Environmental eXtender model, a physically–based hydrological model that requires extensive input data. FIS offers an alternate modeling tool for runoff estimation in the absence of detailed watershed data.