Abstract
Weather forecasting is the scientific procedure of determining the state of the atmosphere considering both time frames and locations. This article devises a novel magnetic feedback artificial tree algorithm-based deep long–short-term memory (MFATA-based deep LSTM) classifier with time-series data. MFATA is the combination of the magnetic optimization algorithm MOA with the feedback artificial tree FAT algorithm for weather forecasting. Here, the feature selection is processed using a Moth Flame Optimization based Bat (MFO-Bat). Then, based on the clustered result, the forecasting process is accomplished using a deep LSTM classifier. Finally, the Taylor series model is used to generate the final forecast result. The proposed method achieved mean square error, root mean square error, mean absolute scaled error and symmetric mean absolute percentage error values of 4.12, 2.03, 0.602 and 56.376, respectively. The approach developed in this study has the potential to be used as an efficient and reliable weather forecasting method.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.