This work proposes a new computational model (CWI) for water quality assessment in freshwater intensive cultured ponds, by using Weighted Fuzzy Inference Systems through a rule categorization process to preserve and help the growing and fish production. Each species unique and its requirements for habitat, environment, and nutrition must adapt to these, but there are elemental physical, chemical parameters which are necessary for coexisting and interact in any aquatic environment, as temperature, dissolved oxygen, pH, total ammonia and non-ionized ammonia. For this reason, these parameters were considered and measured.
To show the proposed index performance, a comparison of CWI index against by the National Sanitation Foundation (NSF) and the Canadian Council of Ministers of the Environment (CCME) was performed. Those are the most representative models for water assessment and can be adjusted to species. First, the NSF provides a good basis for quality assessment when weight is assigned to each parameter, and according to their importance in the ecosystem. However, the NSF score shows high results of water quality, although some parameters are not in optimal conditions. In the same way, CCME constantly shows good water quality. This is because, in its calculation, an average of the parameter set compensates the bad conditions of one parameter with the good of another. In Fig. 2, it is possible to appreciate a great similarity between CWI, NSF and CCME.
In Table 1, the obtained values in measurement 3) and 4) show that all parameters are within allowable values. The results shown by CWI indicate there is a gap of 1.24 points between 3) and 4). Due to higher temperature produces most chemical reactions, and the DO concentration decreased, the CWI had a better assessment in 4). That difference helps to explain why CWI is an alternative, suitable and reliable tool for the aquaculture of any aquatic species. Overall, the results of NSF and CCME show a good result, but the values obtained are not entirely satisfactory, because they do not contemplate the importance of each parameter. CWI was designed for that purpose, enabling the monitoring and the assessment of high-accuracy of species.