Ecological data prediction and visualization system

Abstract

Temporal patterns in ecological data can be visualized and communicated effectively through graphical means. The aim of this study was to develop a data prediction and visualization system based on historical data and thematic map technology to visualize forecast temporal ecological changes. The visualization system consists of prediction and data visualization modules. The prediction module is developed using a hybrid evolutionary algorithm (HEA) to classify and predict noisy ecological data. The visualization module is developed using Dotnet Framework 2.0 to implement thematic cartography for volume visualization. The visualization system is evaluated by its capability in representing the output data on a map, and by predicting the abundance of Chlorophyta based on other water quality parameters. Rules for predicting Chlorophyta abundance had a success rate of almost 90%. The integration of computational data mining using HEA and visualization using thematic maps promises practical solutions and better techniques for forecasting temporal ecological changes, especially when data sets have complex relationships without clear distinction between various variables.