Rainfall Prediction using Data Mining Techniques: A Systematic Literature Review

Aftab, Shabib; Ahmad, Munir; Hameed, Noureen; Bashir, Muhammad Salman; Ali, Iftikhar; Nawaz, Zahid

doi:10.14569/ijacsa.2018.090518

Cited by 36 publications

(21 citation statements)

References 26 publications

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“…Many researchers have used machine learning techniques to resolve the classification problems in various areas including: sentiment analysis [11,12,13,14,15,16], network intrusion detection [17] "in press" [18], [19], rainfall prediction [20,21], and software defect prediction [10], [29] etc.. Some selected studies regarding the software defect predictions are discussed here briefly.…”

Section: Related Workmentioning

confidence: 99%

A Feature Selection based Ensemble Classification Framework for Software Defect Prediction

Iqbal¹,

Aftab²,

Ullah³

et al. 2019

IJMECS

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Software defect prediction is one of the emerging research areas of software engineering. The prediction of defects at early stage of development process can produce high quality software at lower cost. This research contributes by presenting a feature selection based ensemble classification framework which consists of four stages: 1) Dataset selection, 2) Feature Selection, 3) Classification, and 4) Results. The proposed framework is implemented from two dimensions, one with feature selection and second without feature selection. The performance is evaluated through various measures including: Precision, Recall, F-measure, Accuracy, MCC and ROC. 12 Cleaned publically available NASA datasets are used for experiments. The results of both the dimensions of proposed framework are compared with the other widely used classification techniques such as: "Naïve Bayes (NB), Multi-Layer Perceptron (MLP). Radial Basis Function (RBF), Support Vector Machine (SVM), K Nearest Neighbor (KNN), kStar (K*), One Rule (OneR), PART, Decision Tree (DT), and Random Forest (RF)". Results reflect that the proposed framework outperformed other classification techniques in some of the used datasets however class imbalance issue could not be fully resolved.

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Section: Related Workmentioning

confidence: 99%

A Feature Selection based Ensemble Classification Framework for Software Defect Prediction

Iqbal¹,

Aftab²,

Ullah³

et al. 2019

IJMECS

Self Cite

View full text Add to dashboard Cite

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“…It helps in ongoing constructions, flight operations, transportation etc. Aftab [1] et al have made a study on different articles published during 2011 and 2017 and made a comparison on the different data mining techniques to be used as the base line for the early prediction of rainfall. Data mining techniques can be a better tool than the traditional methods for predicting the rainfall at an early stage.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimized Hybrid Soft Computing Model for Weather Predictions in Delhi

Mantri*

2019

IJRTE

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 Abstract: Soft computing techniques have become very popular now-a-days as these techniques have replaced the traditional and statistical prediction mechanisms in weather forecasting, stock market prediction, crop prediction, solar energy prediction, and predictions in physics and chemistry etc. Each model has its advantages and disadvantages. Hybrid soft computing model is the mechanism of designing the models by exploiting the advantages of two or more models and suppressing their disadvantages. If the advantages of two or more number of models will be taken together in the new proposed model, then the accuracy in the prediction will be enhanced with decrease in error rate. This paper intends to design a hybrid model by taking the advantages of J48 Decision Tree and Fuzzy Logic andit is used to predict the weather parameters in Delhi with better accuracy. He is alife member of ISTE and ISCA. He has published 5 books and more than 58 research papers in reputed international journals including Thomson Reuters (SCI and Web of Science) and conferences including IEEE and Springer. His main research work focuses on Cryptography, Software Engineering and Computational Intelligence. He has 28 years of teaching experience.

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“…The final result showed that 1000 epochs giving minimum error rate when compared to 500 and 1500 epochs [15]. Shabib Aftab, Munir Ahmad, Noureen Hameed, Muhammad Salman Bashir, Iftikhar Ali, Zahid Nawaz proposed a systematic literature survey for various rainfall prediction techniques [4]. Sankhadeep Chatterjee, Bimal Datta, Soumya Sen, Nilanjan Dey use Hybrid Neural Network to predict the rainfall with the accuracy of 89.54% [10].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Time series data is the important aspects of modern research in the domain of knowledge discovery [4]. Spatial, temporal and spatio-temporal data is censorious for climate change and climate effect on the environment [5].…”

Section: Introductionmentioning

confidence: 99%

Soft Computing Research For Weather Prediction Using Multilayer Architecture

Shah¹,

Meganathan²,

Kamali³

2019

IJEAT

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Rainfall prediction is helpful for the agriculture sector. Early prediction of drought and torrent situations is achieved through time series data. For the precise prediction, Artificial Neural Network(ANN) technique is used. The rainy dataset is tested using Feed Forward Neural Network(FFNN). The performance of this model is evaluated using Mean Square Error(MSE) and Magnitude of Relative Error(MRE). Better performance achieved when compared with other data mining techniques.

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Rainfall Prediction using Data Mining Techniques: A Systematic Literature Review

Cited by 36 publications

References 26 publications

A Feature Selection based Ensemble Classification Framework for Software Defect Prediction

A Feature Selection based Ensemble Classification Framework for Software Defect Prediction

Optimized Hybrid Soft Computing Model for Weather Predictions in Delhi

Soft Computing Research For Weather Prediction Using Multilayer Architecture

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