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Best optimizer selection for predicting bushfire occurrences using deep learning

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  • Malka N. Halgamuge

    (The University of Melbourne)

  • Eshan Daminda

    (The University of Melbourne)

  • Ampalavanapillai Nirmalathas

    (The University of Melbourne)

Abstract

Natural disasters like bushfires pose a catastrophic threat to the Australia and the world’s territorial areas. This fire spreads in a wide area within seconds, and therefore, it is complicated and challenging to mitigate. To minimize risk and increase resilience, identifying bushfire occurrences beforehand and takes necessary actions is critically important. This study focuses on using deep learning technology for predicting bushfire occurrences using real weather data in any given location. Real-time and off-line weather data was collected using Weather Underground API, from 2012 to 2017 ( $$N= 128{,}329$$ N = 128 , 329 ). The obtained weather data are temperature, dew point, pressure, wind speed, wind direction, humidity, and daily rain. An algorithm was developed to collect this data automatically from any destination. Six different optimizer models were analyzed that use in deep learning technology. Then, the comparison was carried out to identify the best model. Selecting an optimizer for training the neural network, in this case, deep learning is a challenging task. Six best optimizers were chosen to compare and identify the best optimizer to estimate potential fire occurrences in given locations. The six optimizers; Adagrad, Adadelta, RMSprop, Adam, Nadam, and SGD were compared based on their processing time, prediction accuracy and error. Our findings suggest Adagrad optimizer provides less prediction time which is a critical factor for fast-spreading bushfires. Our work provides a data collection model for disaster prediction, which could be utilized to collect climatic characteristics and topographical characteristics in with larger samples. The developed methodology could be utilized as a natural disaster prediction model for precise predictions with less error and processing time using real-time data. This study provides an enhanced understanding of finding the locations that fire starts or spot fires which are more likely to occur, and lead to identifying of fire starts that are more likely to spread. Graphic abstract

Suggested Citation

  • Malka N. Halgamuge & Eshan Daminda & Ampalavanapillai Nirmalathas, 2020. "Best optimizer selection for predicting bushfire occurrences using deep learning," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 103(1), pages 845-860, August.
    • Handle: RePEc:spr:nathaz:v:103:y:2020:i:1:d:10.1007_s11069-020-04015-7
    DOI: 10.1007/s11069-020-04015-7
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    References listed on IDEAS

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    1. Siquan Yang & Haixia He & Weitao Chen & Lizhe Wang, 2018. "Direct tangible damage assessment for regional snowmelt flood disasters with HJ-1 and HR satellite images: a case study of the Altay region, northern Xinjiang, China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 94(3), pages 1099-1116, December.
    2. NESTEROV, Yurii, 2012. "Efficiency of coordinate descent methods on huge-scale optimization problems," LIDAM Reprints CORE 2511, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
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    Cited by:

    1. Senthil Kumar Jagatheesaperumal & Khan Muhammad & Abdul Khader Jilani Saudagar & Joel J. P. C. Rodrigues, 2023. "Automated Fire Extinguishing System Using a Deep Learning Based Framework," Mathematics, MDPI, vol. 11(3), pages 1-18, January.
    2. Taorong Jia & Lixiao Yao & Guoqing Yang & Qi He, 2022. "A Short-Term Power Load Forecasting Method of Based on the CEEMDAN-MVO-GRU," Sustainability, MDPI, vol. 14(24), pages 1-18, December.

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