Bivalve's Growth Conditions in Coastal Ecosystems: A Decision Tree based Analysis

• Author(s): João Reis ; António Pereira ; Luís Paulo Reis
• Date: 2012
• Persistent ID: http://hdl.handle.net/10216/63182
• Origin: Repositório Aberto da Universidade do Porto
• Subject(s): Ciências Tecnológicas; Engenharia; Engenharia de simulação, Ciências Tecnológicas; Tecnologia; Tecnologia da informação
• Document Type: Conference Object
• Language: English

Description

Simulation is a very useful tool to gather new information about an implemented model, because it can run artificial environments instead of putting in risk some entities that are influenced in the real process. The simulation of physical, chemical and biological processes in coastal ecosystems is used as a way to understand the system internal dynamics and to predict its evolution over time, in order to promote behaviors environmentally friendly and to induce effective and efficient management of the ecosystem as a whole. However, there are several ways of translating and interpreting the data provided by the simulation such as applying appropriate data mining models. This paper describes an approach that uses a Decision Tree model to produce intuitive information about the influence of several environmental variables on the growth conditions of bivalve species within an aquaculture exploration in a coastal ecosystem. This information is captured by relating the values of simulated variables, like water temperature or organic matter, with the length of the bivalve’s shell, extrapolating information about the organic or physical conditions that increase or decrease the growth of the bivalve species, and guiding the stakeholders to locations for the best practice of the seeding process. Simulation is a very useful tool to gather new information about an implemented model, because it can run artificial environments instead of putting in risk some entities that are influenced in the real process. The simulation of physical, chemical and biological processes in coastal ecosystems is used as a way to understand the system internal dynamics and to predict its evolution over time, in order to promote behaviors environmentally friendly and to induce effective and efficient management of the ecosystem as a whole. However, there are several ways of translating and interpreting the data provided by the simulation such as applying appropriate data mining models. This paper describes an approach that uses a Decision Tree model to produce intuitive information about the influence of several environmental variables on the growth conditions of bivalve species within an aquaculture exploration in a coastal ecosystem. This information is captured by relating the values of simulated variables, like water temperature or organic matter, with the length of the bivalve’s shell, extrapolating information about the organic or physical conditions that increase or decrease the growth of the bivalve species, and guiding the stakeholders to locations for the best practice of the seeding process.