Improved prediction of monthly streamflow in a mountainous region by Metaheuristic-Enhanced deep learning and machine learning models using hydroclimatic data-Reference-Cited by-同舟云学术

Improved prediction of monthly streamflow in a mountainous region by Metaheuristic-Enhanced deep learning and machine learning models using hydroclimatic data

Published:2023-09-06 Issue:1 Volume:155 Page:205-228
ISSN:0177-798X
Container-title:Theoretical and Applied Climatology
language:en
Short-container-title:Theor Appl Climatol

Author:

Adnan Rana Muhammad,Mirboluki Amin,Mehraein Mojtaba,Malik Anurag,Heddam Salim^ORCID,Kisi Ozgur

Publisher

Springer Science and Business Media LLC

Subject

Atmospheric Science

Link

https://link.springer.com/content/pdf/10.1007/s00704-023-04624-9.pdf

Reference108 articles.

1. Abbot J, Marohasy J (2014) Input selection and optimization for monthly rainfall forecasting in Queensland, Australia, using artificial neural networks. Atmos Res 138:166–178

2. Adamowski J, Fung Chan H, Prasher SO, Ozga‐Zielinski B, Sliusarieva A (2012) Comparison of multiple linear and nonlinear regression, autoregressive integrated moving average, artificial neural network, and wavelet artificial neural network methods for urban water demand forecasting in Montreal, Canada. Water Resour Res 48(1):17332124

3. Ahmad I, Tang D, Wang T, Wang M, Wagan B (2015) Precipitation trends over time using Mann-Kendall and spearman’s rho tests in swat river basin, Pakistan. Adv Meteorol 2015:431860

4. Alarifi IM, Nguyen HM, Naderi Bakhtiyari A, Asadi A (2019) Feasibility of ANFIS-PSO and ANFIS-GA models in predicting thermophysical properties of Al2O3-MWCNT/oil hybrid nanofluid. Materials 12(21):3628

5. Ali M, Deo RC, Downs NJ, Maraseni T (2018) An ensemble-ANFIS based uncertainty assessment model for forecasting multi-scalar standardized precipitation index. Atmos Res 207:155–180

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