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Predicting Rainfall From Weather Observations Using SVM Approach For Identify the Parameter of Fuel Moisture Code as Fire Weather Index

Predicting Rainfall From Weather Observations Using SVM Approach For Identify the Parameter of Fuel Moisture Code as Fire Weather Index


The Fine Fuel Moisture Code (FFMC) is a numeric rating of the dampness substance of litter and other restored fine fills. This code is a pointer of the general simplicity of start and the combustibility of fine fue. In this study we observed the rainfall time series as a parameter to get the index of FFMC. The main goal of this study to predict the amount of rainfall in a particular division or state well in advance. We predict the amount of rainfall using past data to generate the parameter of FFMC using SVM model in North Sumatera. Based on the result, the various visualizations of data are observed in Aek Godang, North Sumatera which helps in implementing the approaches for rainfall prediction to evaluate the parameter of fuel moisture codeas fire weather index. The analysed individual year rainfall patterns for 2017, 2018, 2019, the approximately close means, noticed less standard deviations.


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Detail Information

Bagian Informasi
Pernyataan Tanggungjawab Darwis Robinson Manalu
Pengarang Darwis Robinson Manalu - Personal Name
Opim Salim Sitompul - Personal Name
Herman Mawengkang - Personal Name
Muhammad Zarlis - Personal Name
Edisi Publish
No. Panggil
Subyek
Klasifikasi Machine Learning
Judul Seri
GMD Artikel Jurnal
Bahasa
Penerbit Journal of Theoretical and Applied Information Technology
Tahun Terbit 2021
Tempat Terbit Islamabad, PAKISTAN
Deskripsi Fisik
Info Detil Spesifik

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Citation

. (2021).Predicting Rainfall From Weather Observations Using SVM Approach For Identify the Parameter of Fuel Moisture Code as Fire Weather Index.(Electronic Thesis or Dissertation). Retrieved from https://localhost/etd

 



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