ENIGMA: The Multidisciplinary International Journal

Articles

Vol. 26 No. 01 (2026): Enigma : The Multidisciplinary International Journal

ITERATIVE CLOSEST POINTS ALGORITHM BASED ON CLIMATE CHANGE RAINFALL FORECASTING

Cite this as:
Enigma : The Multidisciplinary International Journal 2026 ;Vol. 26
Published
2026-02-17

Summary

Prediction of rainfall is indeed an essential assignment in today’s world. Agricultural sector principally depends upon the water concluded from the rainfall for filling up the need of water for crop cultivation. Abnormal rainfall always leads to the crisis for the agricultural sector. The effect of unfavorable rainfall can be minimized by accurately predicting it and so on time. A novel solution called Dynamic Regional Combined short-term rainfall Forecasting approach using Multi-layer Perceptron. First, Principal Component Analysis is used to reduce the dimension of thirteen physical factors, which serves as the input of MLP. Second, an Iterative Closest Points algorithm is applied to determine the structure of MLP. The surrounding sites are perceived based on the forecasting site. Finally, to solve the clutter interference which is caused by the extension of the perception range, DRCF is enhanced with several dynamic strategies. Experiments are conducted on data from dataset and we compare DRCF with atmospheric models and other machine learning approaches. The experimental results show that DRCF outperforms existing approaches in both threat score and root mean square error.

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