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Title
Groundwater level forecasting using soft computing techniques
Authors
Keywords
-
Journal
NEURAL COMPUTING & APPLICATIONS
Volume -, Issue -, Pages -
Publisher
Springer Science and Business Media LLC
Online
2019-05-17
DOI
10.1007/s00521-019-04234-5
References
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Related references
Note: Only part of the references are listed.- Forecasting groundwater levels using nonlinear autoregressive networks with exogenous input (NARX)
- (2018) Andreas Wunsch et al. JOURNAL OF HYDROLOGY
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- Modelling long-term groundwater fluctuations by extreme learning machine using hydro-climatic data
- (2017) Meysam Alizamir et al. HYDROLOGICAL SCIENCES JOURNAL-JOURNAL DES SCIENCES HYDROLOGIQUES
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- (2017) Mohammad Taghi Sattari et al. Groundwater
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- (2015) Kasra Mohammadi et al. COMPUTERS AND ELECTRONICS IN AGRICULTURE
- Evaluating groundwater level fluctuation by support vector regression and neuro-fuzzy methods: a comparative study
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- Predicting groundwater level fluctuations with meteorological effect implications—A comparative study among soft computing techniques
- (2013) Jalal Shiri et al. COMPUTERS & GEOSCIENCES
- Prediction and simulation of monthly groundwater levels by genetic programming
- (2013) E. Fallah-Mehdipour et al. Journal of Hydro-environment Research
- Streamflow forecasting by SVM with quantum behaved particle swarm optimization
- (2012) Sudheer Ch et al. NEUROCOMPUTING
- Extreme learning machines: a survey
- (2011) Guang-Bin Huang et al. International Journal of Machine Learning and Cybernetics
- A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer
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- Hybrid neural modeling for groundwater level prediction
- (2010) Nikunja B. Dash et al. NEURAL COMPUTING & APPLICATIONS
- Application and comparison of two prediction models for groundwater levels: A case study in Western Jilin Province, China
- (2008) Z.P. Yang et al. JOURNAL OF ARID ENVIRONMENTS
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