A Fuzzy-Based Approach for Flexible Modeling and Management of Freshwater Fish Farming

Abstract

Most populated developing countries having water resources, like Egypt, are interested in aquaculture since it supplies around 30% of the cheap protein consumed by customers. Increasing the production of aquaculture, specifically fish farming, in such countries represents an essential need. One candidate water resource for freshwater fish farming in Egypt is the Nile River (1530 km long). Yet, this represents a challenging task due to the existing variations in its water quality (WQ) parameters, such as dissolved oxygen, acidity, and temperature, at different sites. Climate change and pollution negatively affect many water quality parameters. This work provides a fuzzy-based approach for modeling WQ requirements for a set of fish types and evaluates the suitability of a water site for farming them. Thus, it greatly helps managing and planning fish farming in a set of water sites. It benefits from the flexibility of fuzzy logic to model the farming requirements of each fish type. Consequently, it evaluates and clusters the water sites with respect to their degrees of suitability for farming various fish types. The illustrative case study considers 27 freshwater sites spread along the Nile River and 17 freshwater fish types. The result incorporates a set of suitable clusters and a set of unsuitable ones for farming each fish type. It greatly helps managing and planning fish farming, to maximize the overall productivity and prevent probable catastrophic damage. In addition, it shows how to enhance each unsuitable site. We believe that eliminating the causes of pollution in the polluted freshwater sites along a water source could cause a significant boom in the cultivation of multiple freshwater fish types.

Keywords

• fuzzy set
• fuzzy modeling
• fish farming
• fish managing
• aquaculture planning
• freshwater quality prediction