A Novel Method of Rainfall Prediction using MLP-FFN and Hybrid Neural Network Algorithm
In one sentence. Hybrid clustering and neural-network model claims superiority over MLP feed-forward networks for rainfall prediction, with no quantitative results given.
Abstract
The present work proposes a cross breed neural system and multilayer perceptron_ feed forward system based model for precipitation forecast. The crossover models are multistep technique. At first, the information is bunched into a sensible number of groups, at that point for each bunch has prepared independently by Neural Network (NN). Also, as a preprocessing stages a component choice stage is incorporated. Feed forward choice calculation is utilized to locate the most reasonable arrangement of highlights for foreseeing precipitation. To set up the creativity of the proposed cross breed forecast model (Hybrid Neural Network or HNN) has been contrasted and two surely understood models in particular multilayer perceptron feed-forward system (MLP-FFN) utilizing diverse execution measurements. The reproduction results have uncovered that the proposed model is essentially superior to conventional strategies in anticipating precipitation.
Labels
- Topic
- Local & station forecasting
- Type
- New model or method
- Method
- Classical ML (trees, GP, linear, kernels)
- Status
- Research
- Identifiers
- DOI:10.35940/ijitee.j9607.0881019 OpenAlex:W4245804322
- Added
- 2026-10-22
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