Feature-Weighting-Based Prediction of Drought Occurrence via Two-Stage Particle Swarm Optimization
| Autor: | Karpagam Sundararajan, Kathiravan Srinivasan |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2023 |
| Předmět: |
Renewable Energy
Sustainability and the Environment Geography Planning and Development Building and Construction Management Monitoring Policy and Law feature weighting multi-objective optimization particle swarm optimization (PSO) standardized precipitation index (SPI) standardized precipitation evapotranspiration index (SPEI) |
| Zdroj: | Sustainability; Volume 15; Issue 2; Pages: 929 |
| ISSN: | 2071-1050 |
| DOI: | 10.3390/su15020929 |
| Popis: | Drought directly affects environmental sustainability. Predicting the drought at the earliest opportunity will help to execute drought mitigation plans. Several drought indices are used to predict the severity of drought across different geographical regions. The two main drought indices used in India for meteorological drought are the standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI). This work is a study to find the ability of above mentioned indices to predict meteorological drought for the state of Tamil Nadu using 62 years of data. The prediction results are evaluated using the performance metrics of precision, recall, f1 score, Matthews correlation coefficient, and accuracy. The dataset is severely imbalanced due to the low number of drought incidence years. Hence there exists a tug of war between precision and recall, so for improving it without affecting one another, a multi-objective optimization process is applied. The prediction performance is improved by using the filter-global-supervised feature weighting and wrapper-global-supervised feature weighting techniques. In the filter-based feature weighting approach, the information gain measure and Pearson correlation coefficient are used as feature weights. For the wrapper-based feature weighting approach, two-stage particle swarm optimization (PSO) is designed to calculate the weights of the features, and the random forest is used as the classifier. This two-stage PSO constructs the best population set for individual objectives and then searches around it to find the best particle so that the multiple contradicting objectives will converge into the best solution easier. When compared to classification without feature weighting, two-stage PSO feature weighting achieves a 45% improvement in precision. However, only a moderate improvement in recall is obtained. According to the findings, SPI3 and SPEI12 should be given more weightage in metrological drought prediction. |
| Databáze: | OpenAIRE |
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