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Effective Features and Hybrid Classifier for Rainfall Prediction

B KavithaRani, A. Govardhan
2014 International Journal of Computational Intelligence Systems  
In this paper, we propose a novel rainfall prediction technique using effective feature indicators and a hybrid technique.  ...  Subsequently, feature matrices are formed based on the preprocessed rainfall data. Once the feature matrix is formed, the prediction is done based on the hybrid classifier.  ...  In this paper, we have developed a novel rainfall prediction technique using effective feature indicators and a hybrid technique.  ... 
doi:10.1080/18756891.2014.960234 fatcat:sa7udlydk5hfhjjty2k53yowte

MODIFICATIONS OF UNSUPERVISED NEURAL NETWORKS FOR SINGLE TRIAL P300 DETECTION

Lukáš Vařeka, Pavel Mautner
2018 Neural Network World  
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.  ...  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.  ...  The current study proposed a novel hybrid neural based approach to build a robust and accurate model for rainfall prediction.  ... 
doi:10.14311/nnw.2018.28.001 fatcat:yjkzbaezxnbc7j37pg6tgsvu6y

Domestic and Foreign Origin Foodstuff Prices Comparison in Selected Retail Chains

Ondřej Škubna, Jaroslav Homolka, Anna Vladimirovna Belova
2017 Agris on-line Papers in Economics and Informatics  
Keywords Short-range rainfall prediction, statistical feature ranking, fuzzy rule induction and prediction accuracy.  ...  Rainfall prediction is an essential and challenging task in hydro-meteorology.  ...  [125] Statistical Feature Ranking and Fuzzy Supervised Learning Approach in Modeling Regional Rainfall Prediction Systems  ... 
doi:10.7160/aol.2017.090210 fatcat:llfi2jdu75fh5h4r7mxy5e6umu

Intelligent decision support system based on rough set and fuzzy logic approach for efficacious precipitation forecast

M. Sudha
2017 Decision Science Letters  
The experimental results revealed the proposed rough fuzzy model as a better rainfall prediction approach for modeling short range rainfall forecast. Growing Science Ltd. All rights reserved. 7  ...  The optimal reduct set constituting the weather parameters; minimum temperature, relative humidity and solar radiation achieved better prediction accuracy than complete feature set and the reducts.  ...  The intent of this research is to investigate potential application hybridization of rough set theory, data mining and fuzzy set theory for daily rainfall prediction.  ... 
doi:10.5267/j.dsl.2016.6.002 fatcat:ophhi2xjhrbx3moqju5r5j44n4

A Novel Method for Rainfall Prediction and Classification using Neural Networks

K. Varada Rajkumar, K. Subrahmanyam
2021 International Journal of Advanced Computer Science and Applications  
For this we propose a new effective hybrid approach for forecasting and classifying rainfall using the neural network and ACO method.  ...  Present rainfall prediction is the challenging task for the researchers and most of the rainfall prediction techniques are fail in accuracy.  ...  In this paper we propose a new effective hybrid approach for forecasting and classifying rainfall using the neural network and ACO method.  ... 
doi:10.14569/ijacsa.2021.0120760 fatcat:mmnd4i63izewpazli243bv2nvi

Rainfall Prediction using Data Mining Techniques: A Systematic Literature Review

Shabib Aftab, Munir Ahmad, Noureen Hameed, Muhammad Salman, Iftikhar Ali, Zahid Nawaz
2018 International Journal of Advanced Computer Science and Applications  
Data mining techniques can effectively predict the rainfall by extracting the hidden patterns among available features of past weather data.  ...  This research contributes by providing a critical analysis and review of latest data mining techniques, used for rainfall prediction.  ...  Parameters of selected classifier were tuned for the best performance and the issue of biased dataset was dealt effectively by Cost-Sensitive SVM.  ... 
doi:10.14569/ijacsa.2018.090518 fatcat:lqf2acdfb5gr3l5isirwycqegy

Editorial for Vol 24, No 2

Vlado Glavinić
2016 Journal of Computing and Information Technology  
This model builds on the optimization of kernel parameters of the SVM classifier using automatic parameter selection, eventually improving the accuracy of the classifier and reducing the training and testing  ...  The considered approaches are grouped into three categories: UML-based, formal and hybrid.  ...  for finding an effective feature subset for modelling efficient rainfall forecast systems.  ... 
doi:10.20532/cit.2016.1003323 fatcat:wrhbm3aqobdirhyesxtgsd4him

Identifying Effective Features and Classifiers for Short Term Rainfall Forecast Using Rough Sets Maximum Frequency Weighted Feature Reduction Technique

Sudha Mohankumar
2016 Journal of Computing and Information Technology  
From the experimental study, relative humidity2 (a4) and solar radiation (a6) have been identified as the effective parameters for modelling rainfall prediction.  ...  This paper introduces a novel rough set based Maximum Frequency Weighted (MFW) feature reduction technique for finding an effective feature subset for modelling an efficient rainfall forecast system.  ...  Experimental results conclude that this investigation has successfully identified the significant features for effective rainfall prediction by the proposed method.  ... 
doi:10.20532/cit.2016.1002715 fatcat:sluaqpci7jbenoiu6yyxjuomjq

Designing Weather Based Crop Insurance Payout Estimation Based on Agro-Meteorological Data using Machine Learning Techniques

2019 International journal of recent technology and engineering  
Thus the proposed technique can support the simultaneous prediction of the insurance payout to be paid in case of adverse weather factors of the selected crop for five districts with high accuracy and  ...  Then By combining the classified neighboring approach with the threshold factors the Rule-based classifier is done to generate the rules to estimate the insurance payout value as per policymakers for the  ...  Many practical data mining systems are used for predicting the insurance payout for the specified crop based on average temperature and rainfall that deal with building the classifier model of the system  ... 
doi:10.35940/ijrte.c4806.098319 fatcat:3oaoakfwinemfnkv2senpyvojm

Rainfall Prediction System Using Machine Learning Fusion for Smart Cities

Atta-ur Rahman, Sagheer Abbas, Mohammed Gollapalli, Rashad Ahmed, Shabib Aftab, Munir Ahmad, Muhammad Adnan Khan, Amir Mosavi
2022 Sensors  
For effective prediction of rainfall, the technique of fuzzy logic is incorporated in the framework to integrate the predictive accuracies of the machine learning techniques, also known as fusion.  ...  This research proposes a novel real-time rainfall prediction system for smart cities using a machine learning fusion technique.  ...  In [23] , researchers proposed a hybrid method for rainfall forecasting by integrating feature extraction and prediction techniques.  ... 
doi:10.3390/s22093504 pmid:35591194 pmcid:PMC9099780 fatcat:hw5pk2gwizayjab2jxn3gnyfw4

Hybrid deep learning approach for multi-stepahead daily rainfall prediction using GCM simulations

Mohd Imran Khan, Rajib Maity
2020 IEEE Access  
Overall, this study establishes the fact that the hybrid Conv1D-MLP model is more effective in capturing the complex relationship between the causal variables and daily variation of rainfall.  ...  Deep Learning (DL) is an effective technique for dealing with complex systems.  ...  Therefore, it can be effectively used for its prediction with a couple of days in advance. • Simulated meteorological variables by climate models can be beneficially used for better rainfall prediction  ... 
doi:10.1109/access.2020.2980977 fatcat:ay7wpg7bg5fszlz2dw7xywu4ti

A Genetic Decomposition Algorithm for Predicting Rainfall within Financial Weather Derivatives

Sam Cramer, Michael Kampouridis, Alex Freitas
2016 Proceedings of the 2016 on Genetic and Evolutionary Computation Conference - GECCO '16  
This paper is interested in creating a new methodology for increasing the predictive accuracy of rainfall within the problem domain of rainfall derivatives.  ...  We compare the performance of our hybrid GP/GA, against MCRP, Radial Basis Function and GP without decomposition.  ...  Due to rainfall features exhibiting very complex and chaotic processes, it is highly unlikely that a single predictor can classify accurately.  ... 
doi:10.1145/2908812.2908894 dblp:conf/gecco/CramerKF16 fatcat:auljfiaemba7lolpim6izv3fki

Prediction of Flash Flood using Rainfall by MLP Classifier

2020 International journal of recent technology and engineering  
This advancement of the prediction system provides cost-effective solutions and better performance.  ...  The model predicts whether "flood may happen or not" based on the rainfall range for particular locations. Indian district rainfall data is used to build the prediction model.  ...  But it may be impossible to predict the rainfall due to climate change. Kaur et al. [9] implemented the hybrid algorithm in standalone and cloud environments for efficiency.  ... 
doi:10.35940/ijrte.f9880.059120 fatcat:miilchtywjeh7iz5s6omry7t34

Rainfall Prediction using Regression Model

2019 International journal of recent technology and engineering  
In this project, we propose new novel methods for predicting monthly rainfall using linear regressionanalysis.  ...  When it comes to weather forecasting, rainfall prediction is one of the most widely used research areas as numerous lives and property damages occur due to this.  ...  In this paper, we have made use of linear regression for classifying the input data and predicting the occurrence of rainfall.  ... 
doi:10.35940/ijrte.b1098.0782s319 fatcat:v4cwv7rmbzdnjjabas2apf252u

Weather Modeling Using Data-Driven Adaptive Rough-Neuro-Fuzzy Approach

M. Sudha
2017 Current World Environment  
To handle this challenging issue, this research intends to apply the fuzzy and ANN theories for developing hybridized adaptive rough-neuro-fuzzy intelligent system.  ...  The artificial ANN Abstract Recently, hybrid data-driven models have become appropriate predictive patterns in various hydrological forecast scenarios.  ...  Acknowledgement I would like to show my gratitude to the school of Information Technology and Engineering, VIT University for resources and supports during the course of this research, and I thank the  ... 
doi:10.12944/cwe.12.2.27 fatcat:7d5c7v3mfvbzbimtsgx5rvryfy
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