Soil Microbial Dynamics Modeling in Fluctuating Ecological Situations by Using Subtractive Clustering and Fuzzy Rule-Based Inference Systems
作者机构：School of Computer and SoftwareNanjing University of Information Science and TechnologyNanjing 210044China Department of Environmental SciencesUniversity of CaliforniaRiversideCA 92521USA
出 版 物：《Computer Modeling in Engineering & Sciences》 (工程与科学中的计算机建模（英文）)
年 卷 期：2017年第113卷第4期
页 面：443-459页
核心收录：
学科分类：07[理学] 0835[工学-软件工程] 0701[理学-数学] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）]
基 金：Nanjing University of Information Science and Technology NUIST
主 题：Phosphate solubilizing bacteria bacterial population ACC-deaminase activity subtractive clustering fuzzy rule-based prediction system
摘 要：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(0.9988)2 R=in the estimation of previous microbial dynamics.
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