Neural network modeling of hydrological systems: A review of implementation techniques

Oyebode, Oluwaseun; Stretch, Derek

doi:10.1111/nrm.12189

Cited by 111Hydrological Forecasting Using AINeural Networks and ApplicationsHydrology and Watershed Management Studies

Neural network modeling of hydrological systems: A review of implementation techniques

Oyebode, Oluwaseun, Stretch, Derek·2019·Natural Resource Modeling

DOI 10.1111/nrm.12189

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Abstract

Abstract This paper presents a review of procedural steps and implementation techniques used in the development of artificial intelligence models, generally referred to as artificial neural networks (ANNs), within the water resources domain. It focusses on identifying different areas wherein ANNs have found application thereby elucidating its advantages and disadvantages as well as various challenges encountered in its use. Results from this review provide useful insights into how the performance of ANNs can be improved and potential areas of application that are yet to be explored in hydrological modeling. Recommendations for Resource Managers Development of integrated and hybrid artificial intelligent tools is critical to achieving improved forecasts in hydrological modeling studies. Further research into comprehending the internal mechanisms of neural networks is required to obtain a practical meaning of each network component deployed to solve real‐world problems. More robust optimization techniques and tools like differential evolution, particle swarm optimization and deep neural nets, are yet to be fully explored in the water resources analysis, and should be given more attention to enhance neural networks aptitude for modeling complex and nonlinear hydrological processes.

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Several studies have focused on water resources conservation by applying ML and DSS, employing techniques such as ANN, SVM, FL, GA, and ANFIS.
“Several studies have focused on water resources conservation by applying ML and DSS, employing techniques such as ANN, SVM, FL, GA, and ANFIS. Key studies in this area include those by Mehr et al. [180], Oyebode and Stretch [90], Sahoo et al. [181], and Valizadeh et al. [182].”
From Smarter eco-cities and their leading-edge artificial intelligence of things solutions for environmental sustainability: A comprehensive systematic review (2024) · cites this paper as [90]

Learning Internal Representations by Error Propagation
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1985) · doi:10.21236/ada164453 · Cited by 16,501 · see citing works
Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications
Maier, H. R., & Dandy, G. C. (2000) · Environmental Modelling & Software · doi:10.1016/s1364-8152(99)00007-9 · Cited by 2,384 · see citing works
Artificial Neural Networks in Hydrology. I: Preliminary Concepts
ASCE Task Committee on Application of Artificial Neural Networks in Hydrology (2000) · Journal of Hydrologic Engineering · doi:10.1061/(asce)1084-0699(2000)5:2(115 · Cited by 1,477 · see citing works
Hydrological modelling using artificial neural networks
Dawson, C. W., & Wilby, R. L. (2001) · Progress in Physical Geography Earth and Environment · doi:10.1177/030913330102500104 · Cited by 904 · see citing works
Methods used for the development of neural networks for the prediction of water resource variables in river systems: Current status and future directions
Maier, H. R., Jain, A., Dandy, G. C., et al. (2010) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2010.02.003 · Cited by 893 · see citing works
A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series
Wang, W., Chau, K., Cheng, C., & Qiu, L. (2009) · Journal of Hydrology · doi:10.1016/j.jhydrol.2009.06.019 · Cited by 824 · see citing works
Artificial neural networks as rainfall-runoff models
Minns, A. W., & HALL, M. J. (1996) · Hydrological Sciences Journal · doi:10.1080/02626669609491511 · Cited by 754 · see citing works
Data-driven modelling: some past experiences and new approaches
SOLOMATINE, D. P., & Ostfeld, A. (2007) · Journal of Hydroinformatics · doi:10.2166/hydro.2008.015 · Cited by 724 · see citing works
Support vector regression for real-time flood stage forecasting
Yu, P., Chen, S., & Chang, I.-F. (2006) · Journal of Hydrology · doi:10.1016/j.jhydrol.2006.01.021 · Cited by 577 · see citing works
Input determination for neural network models in water resources applications. Part 1—background and methodology
Bowden, G. J., Dandy, G. C., & Maier, H. R. (2004) · Journal of Hydrology · doi:10.1016/j.jhydrol.2004.06.021 · Cited by 543 · see citing works
Review of the self-organizing map (SOM) approach in water resources: Analysis, modelling and application
Kalteh, A. M., Hjorth, P., & Berndtsson, R. (2007) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2007.10.001 · Cited by 534 · see citing works
Hybrid neural network models for hydrologic time series forecasting
Jain, A., & Kumar, A. M. (2006) · Applied Soft Computing · doi:10.1016/j.asoc.2006.03.002 · Cited by 473 · see citing works
Evolutionary artificial neural networks: a review
Ding, S., Li, H., Su, C., Yu, J., & Jin, F. (2011) · Artificial Intelligence Review · doi:10.1007/s10462-011-9270-6 · Cited by 411 · see citing works
Rainfall–runoff modeling using artificial neural network coupled with singular spectrum analysis
Wu, C. L., & Chau, K. (2011) · Journal of Hydrology · doi:10.1016/j.jhydrol.2011.01.017 · Cited by 362 · see citing works
Neural networks and M5 model trees in modelling water level–discharge relationship
Bhattacharya, B., & SOLOMATINE, D. P. (2004) · Neurocomputing · doi:10.1016/j.neucom.2004.04.016 · Cited by 317 · see citing works
River flow forecasting: use of phase-space reconstruction and artificial neural networks approaches
Sivakumar, B., Jayawardena, A. W., & Fernando, T. M. K. G. (2002) · Journal of Hydrology · doi:10.1016/s0022-1694(02)00112-9 · Cited by 316 · see citing works
Daily streamflow forecasting by machine learning methods with weather and climate inputs
Rasouli, K., Hsieh, W. W., & Cannon, A. J. (2011) · Journal of Hydrology · doi:10.1016/j.jhydrol.2011.10.039 · Cited by 298 · see citing works
Two decades of anarchy? Emerging themes and outstanding challenges for neural network river forecasting
Abrahart, R. J., Anctil, F., Coulibaly, P., Dawson, C. W., Mount, N. J., See, L., Shamseldin, A. Y., SOLOMATINE, D. P., Toth, E., & Wilby, R. L. (2012) · Progress in Physical Geography Earth and Environment · doi:10.1177/0309133312444943 · Cited by 297 · see citing works
Forecasting daily streamflow using hybrid ANN models
Wang, W., van Gelder, P. H. A. J. M., Vrijling, J. K., et al. (2005) · Journal of Hydrology · doi:10.1016/j.jhydrol.2005.09.032 · Cited by 280 · see citing works
A counterpropagation fuzzy-neural network modeling approach to real time streamflow prediction
Chang, F., & Chen, Y. (2001) · Journal of Hydrology · doi:10.1016/s0022-1694(01)00350-x · Cited by 250 · see citing works
Transforming data into knowledge for improved wastewater treatment operation: A critical review of techniques
Corominas, L., Garrido‐Baserba, M., Villez, K., Olsson, G., Cortés, U., & Manel Poch, A. (2017) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2017.11.023 · Cited by 220 · see citing works
Comparison of neural network methods for infilling missing daily weather records
Coulibaly, P., & Evora, N. (2007) · Journal of Hydrology · doi:10.1016/j.jhydrol.2007.04.020 · Cited by 208 · see citing works
Comparing Sigmoid Transfer Functions for Neural Network Multistep Ahead Streamflow Forecasting
Yonaba, H., Anctil, F., & Fortin, V. (2010) · Journal of Hydrologic Engineering · doi:10.1061/(asce)he.1943-5584.0000188 · Cited by 208 · see citing works
Experimental investigation of the predictive capabilities of data driven modeling techniques in hydrology - Part 1: Concepts and methodology
ELSHORBAGY, A. A., Corzo, G., Srinivasulu, S., et al. (2010) · Hydrology and earth system sciences · doi:10.5194/hess-14-1931-2010 · Cited by 207 · see citing works
A comparative study of artificial neural networks and neuro-fuzzy in continuous modeling of the daily and hourly behaviour of runoff
Aqil, M., Kita, I., Yano, A., et al. (2007) · Journal of Hydrology · doi:10.1016/j.jhydrol.2007.01.013 · Cited by 198 · see citing works
Neural Networks and Wavelet Conjunction Model for Intermittent Streamflow Forecasting
Kişi, Ö. (2009) · Journal of Hydrologic Engineering · doi:10.1061/(asce)he.1943-5584.0000053 · Cited by 178 · see citing works
Input selection and performance optimization of ANN-based streamflow forecasts in the drought-prone Murray Darling Basin region using IIS and MODWT algorithm
Prasad, R., Deo, R. C., Li, Y., & Maraseni, T. (2017) · Atmospheric Research · doi:10.1016/j.atmosres.2017.06.014 · Cited by 173 · see citing works
ANN-based residential water end-use demand forecasting model
Bennett, C. J., Stewart, R. A., & Beal, C. (2012) · Expert Systems with Applications · doi:10.1016/j.eswa.2012.08.012 · Cited by 162 · see citing works
Temporal neural networks for downscaling climate variability and extremes
Dibike, Y., & Coulibaly, P. (2006) · Neural Networks · doi:10.1016/j.neunet.2006.01.003 · Cited by 160 · see citing works
Selection of input variables for data driven models: An average shifted histogram partial mutual information estimator approach
Fernando, T. M. K. G., Maier, H. R., & Dandy, G. C. (2008) · Journal of Hydrology · doi:10.1016/j.jhydrol.2008.10.019 · Cited by 157 · see citing works
Bayesian neural network for rainfall‐runoff modeling
Khan, M. S., & Coulibaly, P. (2006) · Water Resources Research · doi:10.1029/2005wr003971 · Cited by 150 · see citing works
Multi-model data fusion for river flow forecasting: an evaluation of six alternative methods based on two contrasting catchments
Abrahart, R. J., & See, L. (2002) · Hydrology and earth system sciences · doi:10.5194/hess-6-655-2002 · Cited by 145 · see citing works
A Comparative Assessment of Artificial Neural Network, Generalized Regression Neural Network, Least-Square Support Vector Regression, and K-Nearest Neighbor Regression for Monthly Streamflow Forecasting in Linear and Nonlinear Conditions
Modaresi, F., Araghinejad, S., & Ebrahimi, K. (2017) · Water Resources Management · doi:10.1007/s11269-017-1807-2 · Cited by 131 · see citing works
Designing a neural network for predicting the heat transfer and pressure drop characteristics of Ag/water nanofluids in a heat exchanger
Esfe, M. H. (2017) · Applied Thermal Engineering · doi:10.1016/j.applthermaleng.2017.06.046 · Cited by 115 · see citing works
A novel approach to parameter uncertainty analysis of hydrological models using neural networks
Shrestha, D. L., Kayastha, N., & SOLOMATINE, D. P. (2009) · Hydrology and earth system sciences · doi:10.5194/hess-13-1235-2009 · Cited by 110 · see citing works
Application of the Hybrid Artificial Neural Network Coupled with Rolling Mechanism and Grey Model Algorithms for Streamflow Forecasting Over Multiple Time Horizons
Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬, Z. M., Fu, M., Chen, W., et al. (2018) · Water Resources Management · doi:10.1007/s11269-018-1909-5 · Cited by 105 · see citing works
Emotional ANN (EANN) and Wavelet-ANN (WANN) Approaches for Markovian and Seasonal Based Modeling of Rainfall-Runoff Process
Sharghi, E., Nourani, V., Najafi, H., et al. (2018) · Water Resources Management · doi:10.1007/s11269-018-2000-y · Cited by 100 · see citing works
Estimating uncertainty of streamflow simulation using Bayesian neural networks
Zhang, X., Liang, F., Srinivasan, R., & Liew, M. W. V. (2009) · Water Resources Research · doi:10.1029/2008wr007030 · Cited by 95 · see citing works
A multi-objective neural network trained with differential evolution for dynamic economic emission dispatch
Mason, K., Duggan, J., & Howley, E. (2018) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2018.02.021 · Cited by 90 · see citing works
Identifying Prominent Explanatory Variables for Water Demand Prediction Using Artificial Neural Networks: A Case Study of Bangkok
Babel, M. S., & Shinde, V. R. (2010) · Water Resources Management · doi:10.1007/s11269-010-9766-x · Cited by 88 · see citing works
A DE-based approach to no-wait flow-shop scheduling
Qian, B., Wang, L.-Y., Hu, R., Huang, D., & Wang, X. (2009) · Computers & Industrial Engineering · doi:10.1016/j.cie.2009.02.006 · Cited by 86 · see citing works
River suspended sediment concentration modeling using a neural differential evolution approach
Kişi, Ö. (2010) · Journal of Hydrology · doi:10.1016/j.jhydrol.2010.06.003 · Cited by 85 · see citing works
Comparison of data-driven modelling techniques for river flow forecasting
Londhe, S. N., & Charhate, S. (2010) · Hydrological Sciences Journal · doi:10.1080/02626667.2010.512867 · Cited by 82 · see citing works
River flow forecast for reservoir management through neural networks
Baratti, R., Cannas, B., Fanni, A., et al. (2003) · Neurocomputing · doi:10.1016/s0925-2312(03)00387-4 · Cited by 81 · see citing works
Application of radial basis function neural networks to short-term streamflow forecasting
Kagoda, P. A., Ndiritu, J. G., Ntuli, C., & Mwaka, B. (2010) · Physics and Chemistry of the Earth Parts A/B/C · doi:10.1016/j.pce.2010.07.021 · Cited by 78 · see citing works
Neural network ensemble-based parameter sensitivity analysis in civil engineering systems
Cao, M., Pan, L. X., Gao, Y. F., Novák, D., Ding, Z., Lehký, D., & Li, X. (2015) · Neural Computing and Applications · doi:10.1007/s00521-015-2132-4 · Cited by 77 · see citing works
Classification of hydro-meteorological conditions and multiple artificial neural networks for streamflow forecasting
Toth, E. (2009) · Hydrology and earth system sciences · doi:10.5194/hess-13-1555-2009 · Cited by 75 · see citing works
Optimal design of artificial neural networks by a multi-objective strategy: groundwater level predictions
Giustolisi, O., & Simeone, V. (2006) · Hydrological Sciences Journal · doi:10.1623/hysj.51.3.502 · Cited by 73 · see citing works
Prediction of suspended sediment load using ANN GA conjunction model with Markov chain approach at flood conditions
Adib, A., & Mahmoodi, A. (2016) · KSCE Journal of Civil Engineering · doi:10.1007/s12205-016-0444-2 · Cited by 66 · see citing works
Rainfall–runoff modeling using principal component analysis and neural network
Hu, T., Wu, F., & Zhang, X. (2007) · Hydrology research · doi:10.2166/nh.2007.010 · Cited by 64 · see citing works
Nonlinear system identification using memetic differential evolution trained neural networks
Subudhi, B., & Jena, D. (2011) · Neurocomputing · doi:10.1016/j.neucom.2011.02.006 · Cited by 53 · see citing works
Multiple model combination methods for annual maximum water level prediction during river ice breakup
Sun, W., & Trevor, B. (2017) · Hydrological Processes · doi:10.1002/hyp.11429 · Cited by 53 · see citing works
Methods used for quantifying the prediction uncertainty of artificial neural network based hydrologic models
Kasiviswanathan, K. S., & Sudheer, K. P. (2016) · Stochastic Environmental Research and Risk Assessment · doi:10.1007/s00477-016-1369-5 · Cited by 50 · see citing works
Constraints of artificial neural networks for rainfall-runoff modelling: trade-offs in hydrological state representation and model evaluationPreprint
de Vos, N. J., & Rientjes, T. H. M. (2005) · doi:10.5194/hessd-2-365-2005 · Cited by 34 · see citing works
Investigating the extrapolation performance of neural network models in suspended sediment data
Pektaş, A. O., & Ciğizoğlu, H. K. (2017) · Hydrological Sciences Journal · doi:10.1080/02626667.2017.1349316 · Cited by 30 · see citing works
Neural Networks Training Based on Differential Evolution Algorithm Compared with Other Architectures for Weather Forecasting34
Abdul, H., & Elkom, S. (2009) · Cited by 27 · see citing works
River Flow Forecasting Using an Improved Artificial Neural Network
Adeyemo, J. A., Oyebode, O., & Stretch, D. (2017) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-69710-9_13 · Cited by 21 · see citing works
Self-citation
A novel approach for knowledge extraction from Artificial Neural Networks
Londhe, S. N., & Shah, S. (2017) · ISH Journal of Hydraulic Engineering · doi:10.1080/09715010.2017.1409667 · Cited by 18 · see citing works
Long-range forecasting of suspended sediment
Pektaş, A. O., & Ciğizoğlu, H. K. (2017) · Hydrological Sciences Journal · doi:10.1080/02626667.2017.1383607 · Cited by 17 · see citing works
A New Method for Evaporation Modeling: Dynamic Evolving Neural-Fuzzy Inference System
Kişi, Ö., Mansouri, I., & Hu, J. W. (2017) · Advances in Meteorology · doi:10.1155/2017/5356324 · Cited by 15 · see citing works
Artificial Intelligence Techniques for Predicting and Mapping Daily Pan Evaporation
Arunkumar, R., Jothiprakash, V., & Sharma, K. (2017) · Journal of The Institution of Engineers (India) Series A · doi:10.1007/s40030-017-0215-1 · Cited by 12 · see citing works
Reservoir Inflow Forecasting Using Differential Evolution Trained Neural Networks
Oyebode, O., & Adeyemo, J. A. (2014) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-07494-8_21 · Cited by 8 · see citing works
Self-citation
Applications of soft tools to solve hydrological problems for an integrated Indian catchment
Sayagavi, V., & Charhate, S. (2017) · International Journal of Water Resources and Environmental Engineering · doi:10.5897/ijwree2017.0724 · Cited by 2 · see citing works
Towards a Hydrological Research Agenda
Abrahart, R., Kneale, P. E., & See, L. M. (2004) · CRC Press eBooks · doi:10.1201/9780203024119-21 · Cited by 2 · see citing works
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Authors: Oyebode, O., & Stretch, D.
Affiliations: University of KwaZulu-Natal (ZA)
ORCID: Oyebode, Oluwaseun
Stretch, Derek
Venue: Natural Resource Modeling
Publisher: Wiley
Published: 2018-09-09
Volume: 32
Issue: 1
ISSN: 0890-8575 (ISSN-L)
1939-7445
DOI: 10.1111/nrm.12189
OpenAlex: W2891098782
Cited by: 111
References: 65
Citations by year: 2026: 4 · 2025: 13 · 2024: 14 · 2023: 16 · 2022: 23 · 2021: 20 · 2020: 15 · 2019: 5
FWCI: 5.02
Type: journal article
Language: en
Open access: closed
Licence: onlinelibrary.wiley.com/termsAndConditions#vor (vor)
Locations: Natural Resource Modeling (publishedVersion)
Topics: Physical Sciences › Environmental Science › Environmental Engineering › Hydrological Forecasting Using AI
Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
Physical Sciences › Environmental Science › Water Science and Technology › Hydrology and Watershed Management Studies
Keywords: artificial neural networks, hydrological modeling, water resources, neural network implementation, hybrid intelligent systems, hydrological forecasting, deep neural network, differential evolution, particle swarm optimization

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Neural network modeling of hydrological systems: A review of implementation techniques

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