Weather Status Prediction of Dhaka City Using Machine Learning

The majority of today's weather forecasting studies have been focused on complex physical models. These models are usually run on hundreds of nodes in a High-Performance Computing system, which consumes a lot more power. Despite the employment of these costly and complex tools, projections are frequently incorrect due to inaccurate beginning conditions, measurements or a lack of understanding of atmospheric dynamics. Furthermore, solving complex models like this often takes a long time. The Internet of Things has helped any field that deals with technology. Using an IoT device, a prototype based on a machine learning approach is proposed in this study with an efficient framework, and implementation of an automated weather prediction system based on Artificial Neural Network algorithms was designed and developed. This system includes a technologically advanced irrigation system for our convenience. Using ANN in this research, the weather for the next day appears to have been predicted. The evaluation findings suggest that the model’s accuracy is sufficient for existing works and their approaches.

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Predicting Weather Forecasting State Based on Data Mining Classification Algorithms

Kareem

Abdulazeez

Hasan

2021

AJRCoS

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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.

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Weather Status Prediction of Dhaka City Using Machine Learning

Cited by 3 publications

References 8 publications

Comparative Evaluation of Weather Forecasting using Machine Learning Models

Comparative Evaluation of Weather Forecasting using Machine Learning Models

An Efficient Framework and Implementation of a Weather Prediction System

Predicting Weather Forecasting State Based on Data Mining Classification Algorithms

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