{"title":"台湾干旱风险综合评估与绘图:ANP-ANN 集合方法。","authors":"Yuei-An Liou, Trong-Hoang Vo, Duy-Phien Tran, Hai-An Bui","doi":"10.1016/j.scitotenv.2024.175835","DOIUrl":null,"url":null,"abstract":"<p><p>This study aims to comprehensively evaluate and map the risk of drought in Taiwan by employing a combination of two powerful models, the Analytic Network Process (ANP) and Artificial Neural Network (ANN). This innovative approach utilizes an ensemble learning method, where ANP constructs a logical network and assigns weights to various indicators. Subsequently, ANN leverages these weights to train the model effectively. A total of twenty indicators were incorporated into the analysis to create a holistic drought risk map for Taiwan. These indicators are thoughtfully categorized into three essential components: hazard, exposure, and vulnerability, providing a well-defined representation of drought risk. The trained ANN model showcases remarkable accuracy and performance, boasting values of 0.940 for accuracy, 0.946 for precision, 0.938 for recall, 0.942 for the F1 score, and 0.923 for the Kappa Index. These results unequivocally affirm the model's effectiveness in predicting drought risk. Furthermore, the final drought risk map underwent rigorous validation through fieldwork and statistical data. The validation process yielded high accuracies, ranging from 0.717 to 0.851, for assessing damage to crops, converted damaged areas, and estimated value product loss. This validation, conducted against multiple reference data sources, underscores the map's reliability and its alignment with various goodness-of-fit criteria. In summary, this study underscores the potency of the ANP-ANN ensemble approach, with the trained ANN model proving its robustness in swiftly predicting drought risk across diverse ecological and socioeconomic scenarios.</p>","PeriodicalId":422,"journal":{"name":"Science of the Total Environment","volume":" ","pages":"175835"},"PeriodicalIF":8.2000,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Comprehensive drought risk assessment and mapping in Taiwan: An ANP-ANN ensemble approach.\",\"authors\":\"Yuei-An Liou, Trong-Hoang Vo, Duy-Phien Tran, Hai-An Bui\",\"doi\":\"10.1016/j.scitotenv.2024.175835\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>This study aims to comprehensively evaluate and map the risk of drought in Taiwan by employing a combination of two powerful models, the Analytic Network Process (ANP) and Artificial Neural Network (ANN). This innovative approach utilizes an ensemble learning method, where ANP constructs a logical network and assigns weights to various indicators. Subsequently, ANN leverages these weights to train the model effectively. A total of twenty indicators were incorporated into the analysis to create a holistic drought risk map for Taiwan. These indicators are thoughtfully categorized into three essential components: hazard, exposure, and vulnerability, providing a well-defined representation of drought risk. The trained ANN model showcases remarkable accuracy and performance, boasting values of 0.940 for accuracy, 0.946 for precision, 0.938 for recall, 0.942 for the F1 score, and 0.923 for the Kappa Index. These results unequivocally affirm the model's effectiveness in predicting drought risk. Furthermore, the final drought risk map underwent rigorous validation through fieldwork and statistical data. The validation process yielded high accuracies, ranging from 0.717 to 0.851, for assessing damage to crops, converted damaged areas, and estimated value product loss. This validation, conducted against multiple reference data sources, underscores the map's reliability and its alignment with various goodness-of-fit criteria. In summary, this study underscores the potency of the ANP-ANN ensemble approach, with the trained ANN model proving its robustness in swiftly predicting drought risk across diverse ecological and socioeconomic scenarios.</p>\",\"PeriodicalId\":422,\"journal\":{\"name\":\"Science of the Total Environment\",\"volume\":\" \",\"pages\":\"175835\"},\"PeriodicalIF\":8.2000,\"publicationDate\":\"2024-11-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Science of the Total Environment\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://doi.org/10.1016/j.scitotenv.2024.175835\",\"RegionNum\":1,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/8/28 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q1\",\"JCRName\":\"ENVIRONMENTAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Science of the Total Environment","FirstCategoryId":"93","ListUrlMain":"https://doi.org/10.1016/j.scitotenv.2024.175835","RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/8/28 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
Comprehensive drought risk assessment and mapping in Taiwan: An ANP-ANN ensemble approach.
This study aims to comprehensively evaluate and map the risk of drought in Taiwan by employing a combination of two powerful models, the Analytic Network Process (ANP) and Artificial Neural Network (ANN). This innovative approach utilizes an ensemble learning method, where ANP constructs a logical network and assigns weights to various indicators. Subsequently, ANN leverages these weights to train the model effectively. A total of twenty indicators were incorporated into the analysis to create a holistic drought risk map for Taiwan. These indicators are thoughtfully categorized into three essential components: hazard, exposure, and vulnerability, providing a well-defined representation of drought risk. The trained ANN model showcases remarkable accuracy and performance, boasting values of 0.940 for accuracy, 0.946 for precision, 0.938 for recall, 0.942 for the F1 score, and 0.923 for the Kappa Index. These results unequivocally affirm the model's effectiveness in predicting drought risk. Furthermore, the final drought risk map underwent rigorous validation through fieldwork and statistical data. The validation process yielded high accuracies, ranging from 0.717 to 0.851, for assessing damage to crops, converted damaged areas, and estimated value product loss. This validation, conducted against multiple reference data sources, underscores the map's reliability and its alignment with various goodness-of-fit criteria. In summary, this study underscores the potency of the ANP-ANN ensemble approach, with the trained ANN model proving its robustness in swiftly predicting drought risk across diverse ecological and socioeconomic scenarios.
期刊介绍:
The Science of the Total Environment is an international journal dedicated to scientific research on the environment and its interaction with humanity. It covers a wide range of disciplines and seeks to publish innovative, hypothesis-driven, and impactful research that explores the entire environment, including the atmosphere, lithosphere, hydrosphere, biosphere, and anthroposphere.
The journal's updated Aims & Scope emphasizes the importance of interdisciplinary environmental research with broad impact. Priority is given to studies that advance fundamental understanding and explore the interconnectedness of multiple environmental spheres. Field studies are preferred, while laboratory experiments must demonstrate significant methodological advancements or mechanistic insights with direct relevance to the environment.