Skip Nav Destination

Introducing knowledge into learning based on genetic programming

Journal of Hydroinformatics (2009) 11 (3-4): 181–193.

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

Genetic programming for hydrological applications: to model or to forecast that is the question

Herath Mudiyanselage Viraj Vidura Herath
Jayashree Chadalawada
Vladan Babovic

Journal of Hydroinformatics (2021) 23 (4): 740.

A two-stage multiple-point conceptual model to predict river stage-discharge process using machine learning approaches

Farhad Alizadeh
Alireza Faregh Gharamaleki
Rasoul Jalilzadeh

Journal of Water and Climate Change (2021) 12 (1): 278.

Application of gradient tree boosting regressor for the prediction of scour depth around bridge piers

B. M. Sreedhara
Amit Prakash Patil
Jagalingam Pushparaj
Geetha Kuntoji
Sujay Raghavendra Naganna

Journal of Hydroinformatics (2021) 23 (4): 849.

A comparative study of wavelet and empirical mode decomposition-based GPR models for river discharge relationship modeling at consecutive hydrometric stations

Kiyoumars Roushangar
Masoumeh Chamani
Roghayeh Ghasempour
Hazi Mohammad Azamathulla
Farhad Alizadeh

Water Supply (2021) 21 (6): 3080.

A workflow to address pitfalls and challenges in applying machine learning models to hydrology

Amr Gharib
Evan G.R. Davies

Advances in Water Resources (2021) 152: 103920.

Uncertainty analysis of monthly river flow modeling in consecutive hydrometric stations using integrated data-driven models

Karim Amininia
Seyed Mahdi Saghebian

Journal of Hydroinformatics (2021) 23 (4): 897.

Riprap incipient motion for overtopping flows with machine learning models

Mohammad Najafzadeh
Giuseppe Oliveto

Journal of Hydroinformatics (2020) 22 (4): 749.

Prediction of River Stage-Discharge Process Based on a Conceptual Model Using EEMD-WT-LSSVM Approach

Alireza Faregh Gharamaleki
Mojtaba Jalilzadeh
Ali Akhoundzadeh

Water Resources (2020) 47 (1): 41.

Hydrologically Informed Machine Learning for Rainfall‐Runoff Modeling: A Genetic Programming‐Based Toolkit for Automatic Model Induction

Jayashree Chadalawada
H. M. V. V. Herath
Vladan Babovic

Water Resources Research (2020) 56 (4)

Estimation of scour depth around submerged weirs using self-adaptive extreme learning machine

M. Rashki Ghaleh Nou
M. Azhdary Moghaddam
M. Shafai Bajestan
H. Md. Azamathulla

Journal of Hydroinformatics (2019) 21 (6): 1082.

Comparison of automatic and guided learning for Bayesian networks to analyse pipe failures in the water distribution system

Kayu Tang
David J. Parsons
Simon Jude

Reliability Engineering & System Safety (2019) 186: 24.

Hybrid meta-heuristics artificial intelligence models in simulating discharge passing the piano key weirs

Mohammad Zounemat-Kermani
Amin Mahdavi-Meymand

Journal of Hydrology (2019) 569: 12.

Influential parameters on submerged discharge capacity of converging ogee spillways based on experimental study and machine learning-based modeling

Kiyoumars Roushangar
Ali Foroudi
Mojtaba Saneie

Journal of Hydroinformatics (2019) 21 (3): 474.

Interdisciplinary application of numerical and machine-learning-based models to predict half-hourly suspended sediment concentrations during typhoons

Cheng-Chia Huang
Hsi-Ting Fang
Hao-Che Ho
Bing-Chen Jhong

Journal of Hydrology (2019) 573: 661.

Using bootstrap ELM and LSSVM models to estimate river ice thickness in the Mackenzie River Basin in the Northwest Territories, Canada

Rahim Barzegar
Mahsa Ghasri
Zhiming Qi
John Quilty
Jan Adamowski

Journal of Hydrology (2019) 577: 123903.

An improved gene expression programming model for streamflow forecasting in intermittent streams

Ali Danandeh Mehr

Journal of Hydrology (2018) 563: 669.

Modelling of biochemical oxygen demand from limited water quality variable by ANFIS using two partition methods

Belouz Khaled
Aidaoui Abdellah
Dechemi Noureddine
Heddam Salim
Aguenini Sabeha

Water Quality Research Journal (2018) 53 (1): 24.

Genetic programming in water resources engineering: A state-of-the-art review

Ali Danandeh Mehr
Vahid Nourani
Ercan Kahya
Bahrudin Hrnjica
Ahmed M.A. Sattar
Zaher Mundher Yaseen

Journal of Hydrology (2018) 566: 643.

Use of conventional flow resistance equations and a model for the Nikuradse roughness in vegetated flows at high submergence

Paola Gualtieri
Sergio De Felice
Vittorio Pasquino
Guelfo Pulci Doria

Journal of Hydrology and Hydromechanics (2018) 66 (1): 107.

Prediction of scour depth around bridge piers using self-adaptive extreme learning machine

Isa Ebtehaj
Ahmed M. A. Sattar
Hossein Bonakdari
Amir Hossein Zaji

Journal of Hydroinformatics (2017) 19 (2): 207.

Application of spatial-temporal error correction in updating hydrodynamic model

Xuan Wang
Vladan Babovic
Xin Li

Journal of Hydro-environment Research (2017) 16: 45.

HydroUnits: supporting dimensional analysis in hydrologic computing systems using sensor-based standards

Paul Celicourt
Michael Piasecki

Journal of Hydroinformatics (2016) 18 (2): 168.

Application of hybrid Kalman filter for improving water level forecast

Xuan Wang
Vladan Babovic

Journal of Hydroinformatics (2016) 18 (5): 773.

Understanding Water Level Residuals in Malacca Strait Using Genetic Programming

Serene Hui Xin Tay
Vladan Babovic

Procedia Engineering (2016) 154: 1267.

Improved sea level anomaly prediction through combination of data relationship analysis and genetic programming in Singapore Regional Waters

Alamsyah Kurniawan
Seng Keat Ooi
Vladan Babovic

Computers & Geosciences (2014) 72: 94.

Hybrid Wavelet-Postfix-GP Model for Rainfall Prediction of Anand Region of India

Vipul K. Dabhi
Sanjay Chaudhary

Advances in Artificial Intelligence (2014) 2014: 1.

Data-driven dynamic emulation modelling for the optimal management of environmental systems

A. Castelletti
S. Galelli
M. Restelli
R. Soncini-Sessa

Environmental Modelling & Software (2012) 34: 30.

Pipe break prediction based on evolutionary data-driven methods with brief recorded data

Qiang Xu
Qiuwen Chen
Weifeng Li
Jinfeng Ma

Reliability Engineering & System Safety (2011) 96 (8): 942.

Introducing knowledge into learning based on genetic programming

This Feature Is Available To Subscribers Only