{"title":"每日流量预测的数据驱动技术比较","authors":"P. de Bourgoing, A. Malekian","doi":"10.1007/s13762-023-05131-0","DOIUrl":null,"url":null,"abstract":"<div><p>Four artificial intelligence methods are compared for streamflow forecasting. The models are tested using 20 years of daily streamflow values in seven basins of the Zagros Mountain Range, Iran. The models considered in the study are artificial neural networks (ANNs), Artificial Neural Networks trained with Ant Colony Optimization for continuous domains (ACO<sub>ℝ</sub>–ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Multigene Genetic Programming (MGGP). The performances of the models are measured by the root mean square error, the coefficient of determination (<i>R</i><sup>2</sup>) and the Nash–Sutcliffe model efficiency. Depending on the basin, ANN, ANFIS or MGGP is the best performing method. None of the methods outperforms the others for all the basins. Overall, the best-performing model is ANN and the worst is ACO<sub>ℝ</sub>–ANN. The physical and climate characteristics of the basins influence the models’ performances.</p></div>","PeriodicalId":589,"journal":{"name":"International Journal of Environmental Science and Technology","volume":"20 10","pages":"11093 - 11106"},"PeriodicalIF":3.0000,"publicationDate":"2023-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Comparison of data-driven techniques for daily streamflow forecasting\",\"authors\":\"P. de Bourgoing, A. Malekian\",\"doi\":\"10.1007/s13762-023-05131-0\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Four artificial intelligence methods are compared for streamflow forecasting. The models are tested using 20 years of daily streamflow values in seven basins of the Zagros Mountain Range, Iran. The models considered in the study are artificial neural networks (ANNs), Artificial Neural Networks trained with Ant Colony Optimization for continuous domains (ACO<sub>ℝ</sub>–ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Multigene Genetic Programming (MGGP). The performances of the models are measured by the root mean square error, the coefficient of determination (<i>R</i><sup>2</sup>) and the Nash–Sutcliffe model efficiency. Depending on the basin, ANN, ANFIS or MGGP is the best performing method. None of the methods outperforms the others for all the basins. Overall, the best-performing model is ANN and the worst is ACO<sub>ℝ</sub>–ANN. The physical and climate characteristics of the basins influence the models’ performances.</p></div>\",\"PeriodicalId\":589,\"journal\":{\"name\":\"International Journal of Environmental Science and Technology\",\"volume\":\"20 10\",\"pages\":\"11093 - 11106\"},\"PeriodicalIF\":3.0000,\"publicationDate\":\"2023-08-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Environmental Science and Technology\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s13762-023-05131-0\",\"RegionNum\":4,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENVIRONMENTAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Environmental Science and Technology","FirstCategoryId":"93","ListUrlMain":"https://link.springer.com/article/10.1007/s13762-023-05131-0","RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
Comparison of data-driven techniques for daily streamflow forecasting
Four artificial intelligence methods are compared for streamflow forecasting. The models are tested using 20 years of daily streamflow values in seven basins of the Zagros Mountain Range, Iran. The models considered in the study are artificial neural networks (ANNs), Artificial Neural Networks trained with Ant Colony Optimization for continuous domains (ACOℝ–ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Multigene Genetic Programming (MGGP). The performances of the models are measured by the root mean square error, the coefficient of determination (R2) and the Nash–Sutcliffe model efficiency. Depending on the basin, ANN, ANFIS or MGGP is the best performing method. None of the methods outperforms the others for all the basins. Overall, the best-performing model is ANN and the worst is ACOℝ–ANN. The physical and climate characteristics of the basins influence the models’ performances.
期刊介绍：
International Journal of Environmental Science and Technology (IJEST) is an international scholarly refereed research journal which aims to promote the theory and practice of environmental science and technology, innovation, engineering and management.
A broad outline of the journal''s scope includes: peer reviewed original research articles, case and technical reports, reviews and analyses papers, short communications and notes to the editor, in interdisciplinary information on the practice and status of research in environmental science and technology, both natural and man made.
The main aspects of research areas include, but are not exclusive to; environmental chemistry and biology, environments pollution control and abatement technology, transport and fate of pollutants in the environment, concentrations and dispersion of wastes in air, water, and soil, point and non-point sources pollution, heavy metals and organic compounds in the environment, atmospheric pollutants and trace gases, solid and hazardous waste management; soil biodegradation and bioremediation of contaminated sites; environmental impact assessment, industrial ecology, ecological and human risk assessment; improved energy management and auditing efficiency and environmental standards and criteria.
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