Incorporating large-scale atmospheric variables in long-term seasonal rainfall forecasting using artificial neural networks: an application to the Ping Basin in Thailand | Citedby Results | Hydrology Research | IWA Publishing

Skip to Main Content

Skip Nav Destination

Incorporating large-scale atmospheric variables in long-term seasonal rainfall forecasting using artificial neural networks: an application to the Ping Basin in Thailand

Hydrology Research (2017) 48 (3): 867–882.

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

Assessing the Implication of Climate Change to Forecast Future Flood Using SWAT and HEC-RAS Model under CMIP5 Climate Projection in Upper Nan Watershed, Thailand

Muhammad Chrisna Satriagasa
Piyapong Tongdeenok
Naruemol Kaewjampa

Sustainability (2023) 15 (6): 5276.

A Stacking Ensemble Learning Model for Monthly Rainfall Prediction in the Taihu Basin, China

Jiayue Gu
Shuguang Liu
Zhengzheng Zhou
Sergey R. Chalov
Qi Zhuang

Water (2022) 14 (3): 492.

Stability investigation of the flood protection structure at Nava Nakorn industrial estate, Thailand

Menglim Hoy
Artit Udomchai
Chakkrid Yeanyong
Suksun Horpibulsuk
Arul Arulrajah

Engineering Failure Analysis (2022) 137: 106279.

Long-Short Term Memory Technique for Monthly Rainfall Prediction in Thale Sap Songkhla River Basin, Thailand

Nureehan Salaeh
Pakorn Ditthakit
Sirimon Pinthong
Mohd Abul Hasan
Saiful Islam
Babak Mohammadi
Nguyen Thi Thuy Linh

Symmetry (2022) 14 (8): 1599.

Impact of climate change on hydro-climatological parameters in North Cyprus: application of artificial intelligence-based statistical downscaling models

Gozen Elkiran
Vahid Nourani
Ogodor Elvis
Jazuli Abdullahi

Journal of Hydroinformatics (2021) 23 (6): 1395.

Application of artificial neural network and predictor screening method for downscaling climatic parameters

N Rouzegari
V Nourani
A Molajou

IOP Conference Series: Earth and Environmental Science (2020) 491 (1): 012002.

An integrated simulation-optimization framework to optimize the reservoir operation adapted to climate change scenarios

Vahid Nourani
Nazak Rouzegari
Amir Molajou
Aida Hosseini Baghanam

Journal of Hydrology (2020) 587: 125018.

ANN-based statistical downscaling of climatic parameters using decision tree predictor screening method

Vahid Nourani
Zahra Razzaghzadeh
Aida Hosseini Baghanam
Amir Molajou

Theoretical and Applied Climatology (2019) 137 (3-4): 1729.

Coupling the maximum overlap discrete wavelet transform and long short-term memory networks for irrigation flow forecasting

Soukayna Mouatadid
Jan F. Adamowski
Mukesh K. Tiwari
John M. Quilty

Agricultural Water Management (2019) 219: 72.

Effectiveness of CMIP5 Decadal Experiments for Interannual Rainfall Prediction Over Australia

Dipayan Choudhury
Rajeshwar Mehrotra
Ashish Sharma
Alexander Sen Gupta
Bellie Sivakumar

Water Resources Research (2019) 55 (8): 7400.

Novel hybrid linear stochastic with non-linear extreme learning machine methods for forecasting monthly rainfall a tropical climate

Mohammad Zeynoddin
Hossein Bonakdari
Arash Azari
Isa Ebtehaj
Bahram Gharabaghi
Hossein Riahi Madavar

Journal of Environmental Management (2018) 222: 190.

Modelling of runoff and sediment yield using ANN, LS-SVR, REPTree and M5 models

Birendra Bharti
Ashish Pandey
S. K. Tripathi
Dheeraj Kumar

Hydrology Research (2017) 48 (6): 1489.