A comparative study on data mining techniques for rainfall prediction in Subang

Zamani, Nabila Wardah; Khairi, Siti Shaliza Mohd

doi:10.1063/1.5054241

Cited by 13 publications

(8 citation statements)

References 7 publications

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“…The model also showed that the factors that predict rain the most are the average humidity (RH_avg), followed by the minimum temperature (Tn). The hight accuracy achieved by the J48 method is in line with other research which stated that the Decision Tree model is better as compared to the other predictive models [14]. The resulted tree which also shows the…”

Section: Resultssupporting

confidence: 86%

Rain Prediction Using Rule-Based Machine Learning Approach

Anwar

Nugrohadi

Tantriyati

et al. 2020

ASSET

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Rain prediction is an important topic that continues to gain attention throughout the world. The rain has a big impact on various aspects of human life both socially and economically, for example in agriculture, health, transportation, etc. Rain also affects natural disasters such as landslides and floods. The various impact of rain on human life prompts us to build a model to understand and predict rain to provide early warning in various fields/needs such as agriculture, transportation, etc. This research aims to build a rain prediction model using a rule-based Machine Learning approach by utilizing historical meteorological data. The experiment using the J48 method resulted in up to 77.8% accuracy in the training model and gave accurate prediction results of 86% when tested against actual weather data in 2020.

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Section: Resultssupporting

confidence: 86%

Rain Prediction Using Rule-Based Machine Learning Approach

Anwar

Nugrohadi

Tantriyati

et al. 2020

ASSET

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“…Apart from that, 12 out of 23 articles used age as the factor to analyze student performance. [46] explained that as we are aging, there are some physical changes in our brain that make us have more difficulty to remember or learn efficiently. Howbeit, the statement made by [46] raises doubts as several authors believe that age is not a significant predictor of students' performance, as outlined by [3].…”

Section: Related Workmentioning

confidence: 99%

“…[46] explained that as we are aging, there are some physical changes in our brain that make us have more difficulty to remember or learn efficiently. Howbeit, the statement made by [46] raises doubts as several authors believe that age is not a significant predictor of students' performance, as outlined by [3]. This finding is consistent with [47] who discovered that there is no significant relationship between academic performance and age.…”

Section: Related Workmentioning

confidence: 99%

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Review on Predicting Students’ Graduation Time Using Machine Learning Algorithms

Suhaimi¹,

Abdul-Rahman²,

Mutalib³

et al. 2019

IJMECS

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Nowadays, the application of data mining is widely prevalent in the education system. The ability of data mining to obtain meaningful information from meaningless data makes it very useful to predict students' achievement, university's performance, and many more. According to the Department of Statistics Malaysia, the numbers of student who do not manage to graduate on time rise dramatically every year. This challenging scenario worries many parties, especially university management teams. They have to timely devise strategies in order to enhance the students' academic achievement and discover the main factors contributing to the timely graduation of undergraduate students. This paper discussed the factors utilized by other researchers from previous studies to predict students' graduation time and to study the impact of different types of factors with different prediction methods. Taken together, findings of this research confirmed the usefulness of Neural Network and Support Vector Machine as the most competitive classifiers compared with Naï ve Bayes and Decision Tree. Furthermore, our findings also indicate that the academic assessment was a prominent factor when predicting students' graduation time.

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“…For this, we can use many regression and classification techniques to check the overall accuracy and the performance. Since, numerous researches have been conducted on weather forecasting and also keeping this fact in mind that different machine learning techniques produce different accuracies, so it is vital to choose the best algorithm that produces better accuracy and performance and model it as per the requirements [2]. algorithms.…”

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confidence: 99%

An application of logistic model tree (LMT) algorithm to ameliorate Prediction accuracy of meteorological data

Fayaz¹,

Zaman²,

Butt³

2021

IJATEE

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Weather forecasting is considered as one of the approaches which are used to check the state of the atmosphere in the future at a specified location. A type of weather forecasting called as rainfall prediction has the highest influence in the farming & agricultural sector and other various sectors like natural disaster management etc. Accurate and timely rainfall prediction is one of the important factors in today's environment. Since the rainfall parameters keep on changing around the world in different places have different factors which are responsible for the rainfall. These parameters include the wind, vapour pressure, humidity at different intervals, temperature, season, evaporation etc. and are considered as the important meteorological parameters for predicting the rainfall in future time [1].*Author for correspondence Decision tree algorithms have been a choice of tool in the machine learning algorithm over the past decade. It was followed up by its modified version, including the more successful one i.e., random forest. However, there has been more precision and accuracy in modified versions of the various decision tree Re search Article

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A comparative study on data mining techniques for rainfall prediction in Subang

Cited by 13 publications

References 7 publications

Rain Prediction Using Rule-Based Machine Learning Approach

Rain Prediction Using Rule-Based Machine Learning Approach

Review on Predicting Students’ Graduation Time Using Machine Learning Algorithms

An application of logistic model tree (LMT) algorithm to ameliorate Prediction accuracy of meteorological data

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