Investigation on the regional agricultural losses during typhoon periods by artificial neural networks
Date Issued
2010
Date
2010
Author(s)
Cheng, Wei-Guo
Abstract
Taiwan is located in subtropical zone and on the main typhoon track in the northwestern Pacific Ocean, and thus is with frequent typhoons events. On average, typhoons attack Taiwan more than three times per year because of global climate changes. In agriculture, farmers suffered from crop losses and increasing risks of damages due to climate changes. The prediction of rice losses is selected as the goal of this study because its coverage and losses is much more than other agricultural products’ in Taiwan. On the other hand, the impacts of different types of typhoons on Taiwan are also investigated according to the amount of agricultural losses. The content of this study was divided into three steps; first, the characteristic of historical typhoons and the correlation between different types of typhoons and various losses were analyzed. Second, variables highly related to agricultural losses were identified as inputs to artificial neural networks according to aforementioned results. The Self-Organizing feature Map (SOM) network was then applied to classifications which were adopted as the central points of Radial Basis Function Neural Networks (RBFNN). The SORBFNN was built for separately predicting the agricultural losses and rice losses during typhoon periods. The results indicate that the best performance could be obtained when the radius of 7th Beaufort wind scale and typhoon landfall status were replaced by possible coverage. As far as the prediction of rice losses is concerned, the accuracy of model outputs can be increased by adding the membership functions of time and losses besides possible coverage. Finally, the spatial distribution of agricultural losses is significantly distinct because rice was planted in various counties with different environment and social financial conditions. Therefore, Taiwan was classified into three sub-regions and the major climate factors resulting in rice losses in each sub-region were also selected by using Spearman correlation analysis. Results obtained from three regional rice-loss models show that the selected inputs have higher impact on the model built in central region. According to the results achieved, the model is expected to increase the risk management of agricultural losses during typhoon periods and to immediately provide the information on the agricultural subvention when natural disaster occurs.
Subjects
Typhoon
Agricultural losses
Rice losses
Cluster
Artificial neural network
SDGs
Type
thesis
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