Comparative Analysis of Data Mining Techniques for Malaysian Rainfall Prediction

Abstract

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.

Sentiment Analysis of Tweets using SVM

“…The output polarity for each tweet from this algorithm will be compared to the pre-labeled class and then the difference will be calculated by Weka. The performance will be measured in terms of precision, recall and f measure [1], [2], [3], [8].…”

“…In this stage the dataset get normalized and prepared for the classification algorithm so that the particular algorithm can run smoothly and bring effective results in minimum time [8]. According to many researches, parameters for pre-processing includes TF-IDF, Stemmer, stopwords Handler and tokenizer etc [1], [25], [30].…”

Rainfall Prediction in Lahore City using Data Mining Techniques

“…The reason of including the output class in dataset among other features is to analyze the performance and accuracy of data mining techniques [20], [24]. The output result after processing is compared with the known class and performance is measured in terms of precision, recall and f measure [1], [20], [21], [24], [26]. Weka [22], [23] is used in this study for classification and performance analysis.…”

“…Beside the missing values, dataset also contained noise where value resides below or exceeds from a certain limits. For effective data mining results it is recommended to keep the values in a certain limits [1], [11]. Pre-processing of input data is a crucial stage in classification framework which ensures the high accuracy of mining results.…”

“…Researchers in [1] performed a comparative analysis of multiple classifiers for rainfall prediction in Malaysia. Classifiers include Naïve Bayes, Support Vector Machine, Decision Tree, Neural Network and Random Forest.…”