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Title |
Numerical, Machine Learning and Deep-Learning based Framework for Weather Prediction |
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Author |
Bhagwati Sharan, Mohammad Husain, Mohammad Nadeem Ahmed, Anil Kumar Sagar, Arshad Ali, Ahmad Talha Siddiqui, Mohammad Rashid Hussain |
| Citation |
Vol. 24 No. 9 pp. 63-76 |
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Abstract |
Weather forecasting has become a very popular topic nowadays among researchers because of its various effects on global lives. It is a technique to predict the future, what is going to happen in the atmosphere by analyzing various available datasets such as rain, snow, cloud cover, temperature, moisture in the air, and wind speed with the help of our gained scientific knowledge i.e., several approaches and set of rules or we can say them as algorithms that are being used to analyze and predict the weather. Weather analysis and prediction are required to prevent nature from natural losses before it happens by using a Deep Learning Approach. This analysis and prediction are the most challenging task because of having multidimensional and nonlinear data. Several Deep Learning Approaches are available: Numerical Weather Prediction (NWP), needs a highly calculative mathematical equation to gain the present condition of the weather. Quantitative precipitation nowcasting (QPN), is also used for weather prediction. In this article, we have implemented and analyzed the various distinct techniques that are being used in data mining for weather prediction. |
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Keywords |
Data mining; Deep learning; Weather forecasting; NWP; SVM. |
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