Proceedings ArticleDOI
Rainfall prediction using hybrid neural network approach
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TLDR
The proposed two step prediction model (Hybrid Neural Network or HNN) has been compared with MLP-FFN classifier in terms of several statistical performance measuring metrics and the experimental results have suggested a reasonable improvement over traditional methods in predicting rainfall.Abstract:
A novel rainfall prediction method has been proposed. In the present work rainfall prediction in Southern part of West Bengal (India) has been conducted. A two-step method has been employed. Greedy forward selection algorithm is used to reduce the feature set and to find the most promising features for rainfall prediction. First, in the training phase the data is clustered by applying k-means algorithm, then for each cluster a separate Neural Network (NN) is trained. The proposed two step prediction model (Hybrid Neural Network or HNN) has been compared with MLP-FFN classifier in terms of several statistical performance measuring metrics. The data for experimental purpose is collected by Dumdum meteorological station (West Bengal, India) over the period from 1989 to 1995. The experimental results have suggested a reasonable improvement over traditional methods in predicting rainfall. The proposed HNN model outperformed the compared models by achieving 84.26% accuracy without feature selection and 89.54% accuracy with feature selection.read more
Citations
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Journal ArticleDOI
Multi-timescale drought prediction using new hybrid artificial neural network models
TL;DR: In this paper, new hybrid artificial neural network (ANN) models were used for predicting the groundwater resource index (GRI)-based drought at different timescales (6, 12, and 24 months) in Yazd plain, Iran.
Linguistic Hedges Fuzzy Feature Selection for Differential Diagnosis of Erythemato-Squamous Diseases.
TL;DR: In this article, a feature selection based on Linguistic Hedges Neural-Fuzzy classifier is presented for the diagnosis of erythemato-squamous diseases, and the performance evaluation of this system is estimated by using four training-test partition models: 50-50, 60-40, 70-30, and 80-20%.
Proceedings ArticleDOI
Machine Learning based Rainfall Prediction
R. Kingsy Grace,B. Suganya +1 more
TL;DR: The proposed machine learning model provides better results than the other algorithms in the literature and the Mean Square Error, accuracy, correlation are the parameters used to validate the proposed model.
Journal ArticleDOI
Hybrid neural network based rainfall prediction supported by flower pollination algorithm
TL;DR: 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 and revealed that the proposed model is significantly better than traditional methods in predicting rainfall.
Book ChapterDOI
Quantitative Rainfall Prediction: Deep Neural Network-Based Approach
TL;DR: A method which uses the advantages of deep neural network to achieve high degree of performance and accuracy compared to the old conventional ways of forecasting the weather is proposed.
References
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Data Mining: Concepts and Techniques
TL;DR: This book presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects, and provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data.
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An introduction to variable and feature selection
Isabelle Guyon,André Elisseeff +1 more
TL;DR: The contributions of this special issue cover a wide range of aspects of variable selection: providing a better definition of the objective function, feature construction, feature ranking, multivariate feature selection, efficient search methods, and feature validity assessment methods.
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Data clustering: 50 years beyond K-means
TL;DR: A brief overview of clustering is provided, well known clustering methods are summarized, the major challenges and key issues in designing clustering algorithms are discussed, and some of the emerging and useful research directions are pointed out.
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Data Mining Concepts and Techniques
TL;DR: Data mining is the search for new, valuable, and nontrivial information in large volumes of data, a cooperative effort of humans and computers that is possible to put data-mining activities into one of two categories: Predictive data mining, which produces the model of the system described by the given data set, or Descriptive data mining which produces new, nontrivials information based on the available data set.
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