Hybrid Neural Network based rainfall prediction supported by flower pollination algorithm

Title:

Hybrid Neural Network based rainfall prediction supported by flower pollination algorithm

Creator:

Chatterjee, Sankhadeep , Datta, Bimal , and Dey, Nilanjan

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:30139e65-08fd-4026-b590-31b277cff0be
uuid:30139e65-08fd-4026-b590-31b277cff0be
doi:10.14311/NNW.2018.28.027

Subject:

artificial Neural Network,, flower pollination algorithm, rainfall prediction, back propagation, gradient descent, and fuzzy c-means

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

The present work proposes a hybrid neural network based model for rainfall prediction in the Southern part of the state West Bengal of India. The hybrid model is a multistep method. Initially, the data is clustered into a reasonable number of clusters by applying fuzzy c-means algorithm, then for every cluster a separate Neural Network (NN) is trained with the data points of that cluster using well known metaheuristic Flower Pollination Algorithm (FPA). In addition, as a preprocessing phase a feature selection phase is included. Greedy forward selection algorithm is employed to find the most suitable set of features for predicting rainfall. To establish the ingenuity of the proposed hybrid prediction model (Hybrid Neural Network or HNN) has been compared with two well-known models namely multilayer perceptron feed-forward network (MLP-FFN) using different performance metrics. The data set for simulating the model is collected from Dumdum meteorological station (West Bengal, India), recorded with in the 1989 to 1995. The simulation results have revealed that the proposed model is significantly better than traditional methods in predicting rainfall.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2018 Volume:28 | Number:6

Harvested from:

CDK

Metadata only:

false