Open Access

ARTICLE

Soil Microbial Dynamics Modeling in Fluctuating Ecological Situations by Using Subtractive Clustering and Fuzzy Rule-Based Inference Systems

by Sunil Kr. Jha¹, Zulfiqar Ahmad²

1 School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044, China. Email: 002891@nuist.edu.cn.
2 Department of Environmental Sciences, University of California, Riverside, CA 92521, USA

Computer Modeling in Engineering & Sciences 2017, 113(4), 443-459. https://doi.org/10.3970/cmes.2017.113.443

Abstract

Microbial population and enzyme activities are the significant indicators of soil strength. Soil microbial dynamics characterize microbial population and enzyme activities. The present study explores the development of efficient predictive modeling systems for the estimation of specific soil microbial dynamics, like rock phosphate solubilization, bacterial population, and ACC-deaminase activity. More specifically, optimized subtractive clustering (SC) and Wang and Mendel's (WM) fuzzy inference systems (FIS) have been implemented with the objective to achieve the best estimation accuracy of microbial dynamics. Experimental measurements were performed using controlled pot experiment using minimal salt media with rock phosphate as sole carbon source inoculated with phosphate solubilizing microorganism in order to estimate rock phosphate solubilization potential of selected strains. Three experimental parameters, including temperature, pH, and incubation period have been used as inputs SC-FIS and WM-FIS. The better performance of the SC-FIS has been observed as compared to the WM-FIS in the estimation of phosphate solubilization and bacterial population with the maximum value of the coefficient of determination in the estimation of previous microbial dynamics.

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Keywords

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