Name
Abstract | ||
|---|---|---|
A red tide is a temporary natural phenomenon in which harmful algal blooms (HABs) can lead to fin fish and shellfish dying en masse. For example, HABs can damage sea farming on the coast of South Korea, and generally have a bad influence on the coastal environment and sea ecosystem. Prediction of red tide blooms, which consists of a categorical type and a numerical type, can minimize the mitigation cost of HAB disasters and the suffering caused by the damage from red tide events. The first type of prediction has high precision but it represents a simple binary result, and the second can predict how much harm an algal increase causes, but its prediction has lower accuracy than the results of the categorical type. To enhance the automatic forecast of red tide, this paper proposes a red tide prediction method that uses fuzzy reasoning and the ensemble method to obtain prediction results for the categorical and numerical types. The proposed method improves the precision of categorical prediction because the ensemble classifier is enhanced by optimal data of the proposed preprocessing. The method forecasts a numerical prediction, such as the increasing density of red tide algae, using the fuzzy reasoning by which the accuracy of numerical results is improved by the proposed post-processing. The experimental results demonstrate that the proposed method achieves a better red tide prediction performance than other single classifiers. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/s10489-013-0457-1 | Appl. Intell. |
Keywords | Field | DocType |
Enhancing prediction,Red tides prediction,Fuzzy reasoning,Ensemble method,Harmful algal blooms | Algal bloom,Pattern recognition,Fuzzy reasoning,Categorical variable,Computer science,Preprocessor,Artificial intelligence,Classifier (linguistics),Red tide,Binary number,Prediction system | Journal |
Volume | Issue | ISSN |
40 | 2 | 0924-669X |
Citations | PageRank | References |
10 | 0.59 | 8 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sun Park | 1 | 12 | 1.32 |
| Seong Ro Lee | 2 | 152 | 26.32 |