Predicting Weather Forecasting State Based on Data Mining Classification Algorithms
Fairoz Q. Kareem, Adnan Mohsin Abdulazeez, Dathar A. Hasan
Asian Journal of Research in Computer Science · pp. 13–24 · Published 5 Jun 2021
10.9734/ajrcos/2021/v9i330222Abstract
Weather forecasting is the process of predicting the status of the atmosphere for certain regions or locations by utilizing recent technology. Thousands of years ago, humans tried to foretell the weather state in some civilizations by studying the science of stars and astronomy. Realizing the weather conditions has a direct impact on many fields, such as commercial, agricultural, airlines, etc. With the recent development in technology, especially in the DM and machine learning techniques, many researchers proposed weather forecasting prediction systems based on data mining classification techniques. In this paper, we utilized neural networks, Naïve Bayes, random forest, and K-nearest neighbor algorithms to build weather forecasting prediction models. These models classify the unseen data instances to multiple class rain, fog, partly-cloudy day, clear-day and cloudy. These model performance for each algorithm has been trained and tested using synoptic data from the Kaggle website. This dataset contains (1796) instances and (8) attributes in our possession. Comparing with other algorithms, the Random forest algorithm achieved the best performance accuracy of 89%. These results indicate the ability of data mining classification algorithms to present optimal tools to predict weather forecasting.
References (28)
- 1 Classification Based on Decision Tree Algorithm for Machine Learning [DOI]
- 2 Review on Methods to Fix Number of Hidden Neurons in Neural Networks [DOI]
- 3 The Role of Machine Learning Algorithms for Diagnosing Diseases [DOI]
- 4 A rainfall prediction model using artificial neural network [DOI]
- 5 Prediction of weather-induced airline delays based on machine learning algorithms [DOI]
- 6 Solar irradiation prediction with machine learning: Forecasting models selection method depending on weather variability [DOI]
- 7 Application of Data Mining Techniques in Weather Prediction and Climate Change Studies [DOI]
- 8 Gene Selection and Classification of Microarray Data Using Convolutional Neural Network [DOI]
- 9 Machine Learning Classification Based on Radom Forest Algorithm: A Review [DOI]
- 10 Trainable Model Based on New Uniform LBP Feature to Identify the Risk of the Breast Cancer [DOI]
- 11 Gene Expression Classification Based on Deep Learning [DOI]
- 12 K-Nearest Neighbor and Naive Bayes Classifier Algorithm in Determining The Classification of Healthy Card Indonesia Giving to The Poor [DOI]
- 13 Facial Beauty Prediction and Analysis based on Deep Convolutional Neural Network: A Review [DOI]
- 14 Survey on Weather Forecasting Using Data Mining [DOI]
- 15 Analysis of weather data using various regression algorithms [DOI]
- 16 custom: Medical Text Classification Based on Convolutional Neural Network: A Review
- 17 Gene clustering with partition around mediods algorithm based on weighted and normalized mahalanobis distance [DOI]
- 18 Artificial Neural Network based Weather Prediction using Back Propagation Technique [DOI]
- 19 Comparative analysis of Naïve Bayes, K Nearest Neighbor and C.45 method in weather forecast [DOI]
- 20 Data Mining Application on Weather Prediction Using Classification Tree, Naïve Bayes and K-Nearest Neighbor Algorithm With Model Testing of Supervised Learning Probabilistic Brier Score, Confusion Matrix and ROC [DOI]
- 21 Performance Comparison of Data Mining Techniques for Rain Prediction Models in Indonesia [DOI]
- 22 Rainfall Predictive Approach for La Trinidad, Benguet using Machine Learning Classification [DOI]
- 23 Forecasting Meteorological Analysis using Machine Learning Algorithms [DOI]
- 24 Reinforcement Learning and Modeling Techniques: A Review [DOI]
- 25 Predictive Analytics for Weather Forecasting Using Back Propagation and Resilient Back Propagation Neural Networks [DOI]
- 26 Weather Status Prediction of Dhaka City Using Machine Learning [DOI]
- 27 Predictability of Naïve Bayes classifier for lahar hazard mapping by weather radar [DOI]
- 28 Weather Prediction Using Artificial Neural Network
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