Wavelet Extreme Learning Machine (W-ELM) Model   for Drought Index Forecasting

K.N. Singh; Rajeev Ranjan Kumar; Mrinmoy Ray

doi:10.56093/jisas.v77i02.171431

Authors

K.N. Singh ICAR-Indian Agricultural Statistics Research Institute, New Delhi
Rajeev Ranjan Kumar ICAR-Indian Agricultural Statistics Research Institute, New Delhi
Mrinmoy Ray ICAR-Indian Agricultural Statistics Research Institute, New Delhi

https://doi.org/10.56093/jisas.v77i02.171431

Keywords:

Drought forecasting; ELM; SVM; ANN; Wavelet transformation.

Abstract

In an agriculturally depending country like India, accurate and reliable drought forecasting is very important to figure out how drought will affect water resources and agriculture. Data-driven machine learning forecasting techniques are promising approaches for drought forecasting since they take less development time, fewer inputs, and are less sophisticated than dynamic or physical models. Machine learning models for drought forecasting use drought indices that are more operational than raw climatic variables. In this study, the potential of wavelet-based extreme learning machine (W-ELM) model to forecast effective drought index has been explored for Sagar and Chhattarpur districts of the Bundelkhand region of India. The performance of W-ELM model has been compared with the other competitive machine learning models like support vector machine (SVM), extreme learning machine (ELM), and artificial neural network (ANN). Observational outcomes reveals that the W-ELM model outperforms ELM, SVM, and ANN.

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