Improving forecasting accuracy of river flow using gene expression programming based on wavelet decomposition and de-noising | Citedby Results | Hydrology Research | IWA Publishing

Skip to Main Content

Skip Nav Destination

Improving forecasting accuracy of river flow using gene expression programming based on wavelet decomposition and de-noising

Hydrology Research (2018) 49 (3): 711–723.

This article has been cited by the following articles in journals that are participating in CrossRef Cited-by Linking.

Experimental Study to Estimate Hyporheic Velocity Using Wavelet-Hybrid Soft-Computing Model

Fazeleh Kabiri
Mohammad Reza Majdzadeh Tabatabai
Sevda Mozaffari
Mohammad Shayannejad

Water Resources Management (2024) 38 (3): 915.

Multivariate modeling of agricultural river water abstraction via novel integrated-wavelet methods in various climatic conditions

Alireza Emadi
Reza Sobhani
Hossein Ahmadi
Arezoo Boroomandnia
Sarvin Zamanzad-Ghavidel
Hazi Mohammad Azamathulla

Environment, Development and Sustainability (2022) 24 (4): 4845.

Flow Discharge Prediction Study Using a CFD-Based Numerical Model and Gene Expression Programming

Sevda Mozaffari
Erfan Amini
Hossein Mehdipour
Mehdi Neshat

Water (2022) 14 (4): 650.

Considering ecological flow in multi-objective operation of cascade reservoir systems under climate variability with different hydrological periods

Xiaorong Lu
Xuelei Wang
Xuan Ban
Vijay P. Singh

Journal of Environmental Management (2022) 309: 114690.

The utilisation of conceptual and data-driven models for hydrological modelling in semi-arid and humid areas of the Antalya basin in Turkey

Cenk Sezen
Turgay Partal

Acta Geophysica (2022) 70 (2): 897.

An improved long short-term memory network for streamflow forecasting in the upper Yangtze River

Shuang Zhu
Xiangang Luo
Xiaohui Yuan
Zhanya Xu

Stochastic Environmental Research and Risk Assessment (2020) 34 (9): 1313.

A Multivariate and Multistage Medium- and Long-Term Streamflow Prediction Based on an Ensemble of Signal Decomposition Techniques with a Deep Learning Network

Muhammad Sibtain
Xianshan Li
Snoober Saleem

Advances in Meteorology (2020) 2020: 1.

Reservoir Inflow Prediction by Ensembling Wavelet and Bootstrap Techniques to Multiple Linear Regression Model

Adnan Bashir
Muhammad Ahmed Shehzad
Ijaz Hussain
Muhammad Ishaq Asif Rehmani
Sajjad Haider Bhatti

Water Resources Management (2019) 33 (15): 5121.

Daily river flow forecasting using ensemble empirical mode decomposition based heuristic regression models: Application on the perennial rivers in Iran and South Korea

Mohammad Rezaie-Balf
Sungwon Kim
Hossein Fallah
Sina Alaghmand

Journal of Hydrology (2019) 572: 470.