Rainfall Prediction using Data Mining Techniques

Joseph, Jyothis; Ratheesh, T. K.

doi:10.5120/14467-2750

Cited by 30 publications

(12 citation statements)

References 4 publications

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“…Several approaches have been proposed for rainfall forecasting for many locations such as Korea, China, South Africa and others [7], [8], [9]. The current techniques for rainfall prediction including Neural Network [10], K-Nearest Neighbor and Naive Bayes [11], Support Vector Machine [12] and others. Hence, there is a need to investigate multiple techniques in order to identify the best performance in terms of rainfall prediction.…”

Section: Introductionmentioning

confidence: 99%

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Comparative Analysis of Data Mining Techniques for Malaysian Rainfall Prediction

Zainudin¹,

Jasim²,

Bakar³

2016

International Journal on Advanced Science, Engineering and Information Technology

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Climate change prediction analyses the behaviours of weather for a specific time. Rainfall forecasting is a climate change task where specific features such as humidity and wind will be used to predict rainfall in specific locations. Rainfall prediction can be achieved using classification task under Data Mining. Different techniques lead to different performances depending on rainfall data representation including representation for long term (months) patterns and short-term (daily) patterns. Selecting an appropriate technique for a specific duration of rainfall is a challenging task. This study analyses multiple classifiers such as Naïve Bayes, Support Vector Machine, Decision Tree, Neural Network and Random Forest for rainfall prediction using Malaysian data. The dataset has been collected from multiple stations in Selangor, Malaysia. Several pre-processing tasks have been applied in order to resolve missing values and eliminating noise. The experimental results show that with small training data (10%) from 1581 instances Random Forest correctly classified 1043 instances. This is the strength of an ensemble of trees in Random Forest where a group of classifiers can jointly beat a single classifier.

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Section: Introductionmentioning

confidence: 99%

“…A hybrid method of feature extraction and prediction technique for predicting daily rainfall data collected from National Oceanic and Atmospheric Administration (NOAA) for more than 50 years was proposed by [12]. Basically, the features consist of humidity, pressure, temperature and wind speed.…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Data Mining Techniques for Malaysian Rainfall Prediction

Zainudin¹,

Jasim²,

Bakar³

2016

International Journal on Advanced Science, Engineering and Information Technology

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“…Rainfall forecasting gained the attention of many researchers' as it has interesting challenges represented by the complexity that lies beneath predicting specific factors that are linked to rainfall like wind and humidity [21][22][23], [35][36]. Current techniques for rainfall prediction utilize only individual classifiers such as neural networks [21], the knearest neighbours [22], support vector machine [22] and others.…”

Section: ) Statistical Reasoningmentioning

confidence: 99%

Ensemble Learning for Rainfall Prediction

Sani¹,

Rahman²,

Adam³

et al. 2020

IJACSA

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Climate change research is a discipline that analyses the varying weather patterns for a particular period of time. Rainfall forecasting is the task of predicting particular future rainfall amount based on the measured information from the past, including wind, humidity, temperature, and so on. Rainfall forecasting has recently been the subject of several machine learning (ML) techniques with differing degrees of both short-term and also long-term prediction performance. Although several ML methods have been suggested to improve rainfall forecasting, the task of appropriate selection of technique for specific rainfall durations is still not clearly defined. Therefore, this study proposes an ensemble learning to uplift the effectiveness of rainfall prediction. Ensemble learning as an approach that combines multiple ML multiple rainfall prediction classifiers, which include Naïve Bayes, Decision Tree, Support Vector Machine, Random Forest and Neural Network based on Malaysian data. More specifically, this study explores three algebraic combiners: average probability, maximum probability, and majority voting. An analysis of our results shows that the fused ML classifiers based on majority voting are particularly effective in boosting the performance of rainfall prediction compared to individual classification.

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“…Salah satu permasalahan yang paling menantang baik secara ilmiah dan juga teknologi di seluruh dunia saat ini adalah peramalan/prediksi kondisi cuaca dengan hasil seakurat mungkin [1], termasuk didalamnya adalah curah hujan, hal tersebut karena peramalan cuaca terkait dengan kepentingan aktivitas manusia dan disebabkan oportunisme yang tercipta oleh kemajuan teknologi yang terkait dengan bidang penelitian ini, seperti evolusi perhitungan dan peningkatan sistem pengukuran [2].…”

Section: Pendahuluanunclassified

Penggunaan Stacking Classifier Untuk Prediksi Curah Hujan

Sidik

Sen

2019

itfs

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Curah hujan sebagai bentuk informasi dari data meteorologis, penting dalam segala kegiatan manusia yang berhubungan dengan alam, oleh karena itu prediksi atas curah hujan dengan hasil yang akurat merupakan hal yang sangat penting. Salah satu metode yang digunakan untuk prediksi/klasifikasi curah hujan adalah data mining dengan berbagai algoritma dan parameter data yang berbeda. Pada penelitian ini digunakan penggabungan metode klasifikasi dengan Teknik Ensemble Stacking/Stacked Generalization yang menggunakan Naïve Bayes dan C4.5 sebagai base learner dan KNN sebagai meta learner untuk klasifikasi curah hujan. Dataset yang dipergunakan adalah data klimatologi harian yang diambil dari website resmi BMKG (Badan Meteorologi, Klimatologi, Dan Geofisika) untuk stasiun UPT Bandung, Bogor, Citeko dan Jatiwangi dari periode 01 Januari 2000 sampai dengan 31 Desember 2018. Dengan menggunakan tiga skenario pengujian dan validasi menggunakan 10 fold cross validation diperoleh bahwa metode stacking dapat meningkatkan akurasi dari base classifier.

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Rainfall Prediction using Data Mining Techniques

Cited by 30 publications

References 4 publications

Comparative Analysis of Data Mining Techniques for Malaysian Rainfall Prediction

Comparative Analysis of Data Mining Techniques for Malaysian Rainfall Prediction

Ensemble Learning for Rainfall Prediction

Penggunaan Stacking Classifier Untuk Prediksi Curah Hujan

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