Red tides prediction using fuzzy inference and decision tree

A red tide is a temporary natural phenomenon in which harmful algal blooms (HABs) can lead to finfish and shellfish dying en masse. Prediction of red tide bloom consists of a categorical type and a numerical type, which can minimize the mitigation cost of HAB disasters and the suffering caused by th...

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Bibliographic Details
Main Authors: Sun Park, Jong Geun Jeong, JangWoo Kwon, Seong Ro Lee
Format: Conference Proceeding
Language:English
Subjects:
Accuracy
Algae
decision tree
Decision trees
Equations
fuzzy inference
Fuzzy logic
Mathematical model
prediction
Red tide blooms
Tides
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Summary:A red tide is a temporary natural phenomenon in which harmful algal blooms (HABs) can lead to finfish and shellfish dying en masse. Prediction of red tide bloom consists of a categorical type and a numerical type, which can minimize the mitigation cost of HAB disasters and the suffering caused by the damage from red tide events. The categorical prediction has high precision but it represents a simple binary result, and the numerical prediction can predict how much harm an algal increase causes, but its prediction has lower accuracy than the results of the categorical type. This paper proposes a red tide prediction method that combines fuzzy inference with decision tree to obtain prediction results of the categorical and numerical types. The experimental results demonstrate that the proposed method achieves a better red tide prediction performance than other prediction methods by classifiers.
ISSN:2162-1233
DOI:10.1109/ICTC.2012.6387184