Learning from Multiple Models Using Artificial Intelligence to Improve Model Prediction Accuracies: Application to River Flows
出版年份 2018 全文链接
标题
Learning from Multiple Models Using Artificial Intelligence to Improve Model Prediction Accuracies: Application to River Flows
作者
关键词
Multiple Models (MM), Two-level MM strategy, Monthly river flow records, Scatter of error residuals, Distressed Lake Urmia
出版物
WATER RESOURCES MANAGEMENT
Volume 32, Issue 13, Pages 4201-4215
出版商
Springer Nature America, Inc
发表日期
2018-07-27
DOI
10.1007/s11269-018-2038-x
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- (2016) Mohammad Ali Ghorbani et al. Environmental Earth Sciences
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- Prediction of Daily Dewpoint Temperature Using a Model Combining the Support Vector Machine with Firefly Algorithm
- (2016) Eiman Tamah Al-Shammari et al. JOURNAL OF IRRIGATION AND DRAINAGE ENGINEERING
- Application of soft computing based hybrid models in hydrological variables modeling: a comprehensive review
- (2016) Farzad Fahimi et al. THEORETICAL AND APPLIED CLIMATOLOGY
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- (2013) Tao Xiong et al. KNOWLEDGE-BASED SYSTEMS
- A Support Vector Machine-Firefly Algorithm based forecasting model to determine malaria transmission
- (2013) Sudheer Ch et al. NEUROCOMPUTING
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- (2012) Sudheer Ch et al. NEUROCOMPUTING
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- (2011) Rahman Khatibi et al. JOURNAL OF HYDROLOGY
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- (2011) Rahman Khatibi et al. JOURNAL OF HYDROLOGY
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- (2010) Xin She Yang International Journal of Bio-Inspired Computation
- Petrophysical data prediction from seismic attributes using committee fuzzy inference system
- (2009) Ali Kadkhodaie-Ilkhchi et al. COMPUTERS & GEOSCIENCES
- A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series
- (2009) Wen-Chuan Wang et al. JOURNAL OF HYDROLOGY
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