Title
Immediate water quality assessment in shrimp culture using fuzzy inference systems
Abstract
The continuous monitoring of physical, chemical and biological parameters in shrimp culture is an important activity for detecting potential crisis that can be harmful for the organisms. Water quality can be assessed through toxicological tests evaluated directly from water quality parameters involved in the ecosystem; these tests provide an indicator about the water quality. The aim of this study is to develop a fuzzy inference system based on a reasoning process, which involves aquaculture criteria established by official organizations and researchers for assessing water quality by analyzing the main factors that affect a shrimp ecosystem. We propose to organize the water quality parameters in groups according to their importance; these groups are defined as daily, weekly and by request monitoring. Additionally, we introduce an analytic hierarchy process to define priorities for more critical water quality parameters and groups. The proposed system analyzes the most important parameters in shrimp culture, detects potential negative situations and provides a new water quality index (WQI), which describes the general status of the water quality as excellent, good, regular and poor. The Canadian water quality and other well-known hydrological indices are used to compare the water quality parameters of the shrimp water farm. Results show that WQI index has a better performance than other indices giving a more accurate assessment because the proposed fuzzy inference system integrates all environmental behaviors giving as result a complete score. This fuzzy inference system emerges as an appropriated tool for assessing site performance, providing assistance to improve production through contingency actions in polluted ponds.
Year
DOI
Venue
2012
10.1016/j.eswa.2012.02.141
Expert Syst. Appl.
Keywords
Field
DocType
new water quality index,water quality,immediate water quality assessment,fuzzy inference system,proposed system,shrimp culture,water quality parameter,proposed fuzzy inference system,shrimp water farm,critical water quality parameter,canadian water quality,analytic hierarchy process,shrimp,aquaculture
General status,Computer science,Fuzzy inference,Operations research,Continuous monitoring,Artificial intelligence,Contingency,Machine learning,Analytic hierarchy process,Water quality,Fuzzy inference system,Shrimp
Journal
Volume
Issue
ISSN
39
12
0957-4174
Citations 
PageRank 
References 
3
0.42
6
Authors
4