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Amir, Fail
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Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Literature Study on the Development of Neural Networks For Weather Forecasting Amir, Fail; Utami, Ema; Hanafi, Hanafi
Jurnal Teknologi Vol 17 No 1 (2024): Jurnal Teknologi
Publisher : Jurnal Teknologi, Fakultas Teknik, Universitas AKPRIND Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34151/jurtek.v17i1.4637

Abstract

Weather prediction has always been crucial for individuals to make informed decisions and protect themselves from potential hazards. Achieving accurate weather forecasts has historically been a significant challenge. Modern weather forecasting has evolved to integrate sophisticated computer models, data from atmospheric balloons and satellites, and insights from local observations. These methods have resulted in fairly precise predictions. Most forecasting models depend on complex mathematical formulas, but Artificial Neural Networks (ANN) offer a dynamic alternative, adapting their structure based on incoming data. This research aimed to thoroughly evaluate the effectiveness of ANNs in weather prediction. It explored the advantages of ANNs over traditional models, reviewed a range of methodologies, and documented the latest advancements in the field. The ultimate goal was to consolidate research findings to highlight the strides made in enhancing weather forecasting through ANNs.
Literature Study on the Development of Neural Networks For Weather Forecasting Amir, Fail; Utami, Ema; Hanafi, Hanafi
Jurnal Teknologi Vol 17 No 1 (2024): Jurnal Teknologi
Publisher : Jurnal Teknologi, Fakultas Teknik, Universitas AKPRIND Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34151/jurtek.v17i1.4637

Abstract

Weather prediction has always been crucial for individuals to make informed decisions and protect themselves from potential hazards. Achieving accurate weather forecasts has historically been a significant challenge. Modern weather forecasting has evolved to integrate sophisticated computer models, data from atmospheric balloons and satellites, and insights from local observations. These methods have resulted in fairly precise predictions. Most forecasting models depend on complex mathematical formulas, but Artificial Neural Networks (ANN) offer a dynamic alternative, adapting their structure based on incoming data. This research aimed to thoroughly evaluate the effectiveness of ANNs in weather prediction. It explored the advantages of ANNs over traditional models, reviewed a range of methodologies, and documented the latest advancements in the field. The ultimate goal was to consolidate research findings to highlight the strides made in enhancing weather forecasting through ANNs.
Co-Authors Ema Utami Hanafi Hanafi