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A comprehensive survey on conventional and modern neural networks: application to river flow forecasting

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Abstract

This study appraises different types of conventional (e.g., GRNN, RBNN, & MLPNN) and modern neural networks (e.g., integrative, inclusive, hybrid, & recurrent) in forecasting daily flow in the Thames River located in the United Kingdom. The models are mathematically, statistically, and diagnostically compared based on the forecasted results for ten different time-series assortments. The results indicate that all the neural network models acceptably forecasted the daily flow rate, with mean values of R2 > 0.92 and RMSE < 18.6 m3/s. Despite the fact that the integrative neural network models slightly acted better in forecasting flow rate (mean values of R2 > 0.94 and RMSE < 15.3 m3/s), they were not as computationally effective as the other applied models.

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Abbreviations

ABC:

Artificial Bee Colony

ACF:

Autocorrelation Function

ACO:

Ant Colony Optimization

ANFIS:

Adaptive Neuro-Fuzzy Inference System

ANN:

Artificial Neural Network

ARIMA:

AutoRegressive Integrated Moving Average

DE:

Differential Evolution

DWT:

Discrete Wavelet Transform

ELM:

Extreme Learning Machine

FIS:

Fuzzy Inference System

GA:

Genetic Algorithm

GEP:

Gene Expression Programming

GMDH:

Group Method of Data Handling

IA:

Index of Agreement

LSTM:

Long Short-Term Memory

M:

The number of performance measures

MAE:

Mean Absolute Error

MLPNN:

Multi-Layer Perceptron Neural Network

MLR:

Multiple Linear Regression

MNN:

Modular Neural Network

MODWT:

Maximal Overlap Discrete wavelet Transform

N:

The number of the data samples

NARX:

Nonlinear Auto-Regressive model with eXogenous inputs

NFNN:

Neuro-Fuzzy Neural Network

NSE:

Nash-Sutcliffe Efficiency

PACF:

Partial Autocorrelation Function

PSO:

particle swarm optimization

Q:

Daily river flow

Qf :

Forecasted river flow value

Qo :

Measured river flow rate

R2 :

Coefficient of determination

RBNN:

Radial Basis function Neural Network

RMSE:

Root Mean Square Error

RNN:

Recurrent Neural Networks

RSD:

Relative Standard Deviation

SES:

Simple Exponential Smoothing

SI:

Synthesis Index

Std:

The standard deviation

SVM:

Support Vector Machine

TS:

Takagi-Sugeno

WNN:

Wavelet-based Neural Networks

References

  • Abrahart RJ, See LM, Solomatine DP (2008, 68) Practical hydroinformatics: computational intelligence and technological developments in water applications. Springer Science & Business Media

  • Adnan RM, Zounemat-Kermani M, Kuriqi A, Kisi O (2021) Machine learning method in prediction streamflow considering periodicity component. In: Intelligent Data Analytics for Decision-Support Systems in Hazard Mitigation. Springer, Singapore, pp 383–403

    Chapter  Google Scholar 

  • Azad A, Farzin S, Kashi H, Sanikhani H, Karami H, Kisi O (2018) Prediction of river flow using hybrid neuro-fuzzy models. Arab J Geosci 11(22):718

    Article  Google Scholar 

  • Banihabib ME, Bandari R, Peralta RC (2019) Auto-regressive neural-network models for long Lead-time forecasting of daily flow. Water Resour Manag 33(1):159–172

    Article  Google Scholar 

  • Bell VA, Kay AL, Cole SJ, Jones RG, Moore RJ, Reynard NS (2012) How might climate change affect river flows across the Thames Basin? An area-wide analysis using the UKCP09 regional climate model ensemble. J Hydrol 442:89–104

    Article  Google Scholar 

  • Campolo M, Andreussi P, Soldati A (1999) River flood forecasting with a neural network model. Water Resour Res 35(4):1191–1197

    Article  Google Scholar 

  • Chau KW (2006) Particle swarm optimization training algorithm for ANNs in stage prediction of Shing Mun River. J Hydrol 329(3–4):363–367

    Article  Google Scholar 

  • Chen J, Jin Q, Chao J (2012) Design of deep belief networks for short-term prediction of drought index using data in the Huaihe river basin. Math Probl Eng 2012:1–16

    Google Scholar 

  • Chen L, Guo S (2019) Uncertainty analysis of hydrologic forecasts based on copulas. In: Copulas and Its Application in Hydrology and Water Resources. Springer, Singapore, pp 165–210

    Chapter  Google Scholar 

  • Chen XY, Chau KW, Busari AO (2015) A comparative study of population-based optimization algorithms for downstream river flow forecasting by a hybrid neural network model. Eng Appl Artif Intell 46:258–268

    Article  Google Scholar 

  • Cigizoglu HK, Alp M (2006) Generalized regression neural network in modelling river sediment yield. Adv Eng Softw 37(2):63–68

    Article  Google Scholar 

  • Darbandi S, Pourhosseini FA (2018) River flow simulation using a multilayer perceptron-firefly algorithm model. Appl Water Sci 8(3):85

    Article  Google Scholar 

  • Dawson CW, Wilby RL (1999) A comparison of artificial neural networks used for river forecasting. Hydrol Earth Syst Sci 3(4):529–540

    Article  Google Scholar 

  • Delafrouz H, Ghaheri A, Ghorbani MA (2018) A novel hybrid neural network based on phase space reconstruction technique for daily river flow prediction. Soft Comput 22(7):2205–2215

    Article  Google Scholar 

  • Ghorbani MA, Khatibi R, Karimi V, Yaseen ZM, Zounemat-Kermani M (2018) Learning from multiple models using artificial intelligence to improve model prediction accuracies: application to river flows. Water Resour Manag 32(13):4201–4215

    Article  Google Scholar 

  • Ghorbani MA, Zadeh HA, Isazadeh M, Terzi O (2016) A comparative study of artificial neural network (MLP, RBF) and support vector machine models for river flow prediction. Environ Earth Sci 75(6):476

    Article  Google Scholar 

  • Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780

  • Hong WC (2008) Rainfall forecasting by technological machine learning models. Appl Math Comput 200(1):41–57

    Google Scholar 

  • Hussain D, Ahmed Khan A (2020) Machine learning techniques for monthly river flow forecasting of Hunza River, Pakistan. Earth Sci Inf 13:939–949

    Article  Google Scholar 

  • Hussain D, Hussain T, Ahmed Khan A, Asad Naqvi SA, Jamil A (2020) A deep learning approach for hydrological time-series prediction: a case study of Gilgit river basin. Earth Sci Inf 13:915–927

    Article  Google Scholar 

  • Hyndman RJ, Athanasopoulos G (2018) Forecasting: principles and practice. OTexts, Melbourne https://otexts.org/fpp2

    Google Scholar 

  • Ikeda S, Ochiai M, Sawaragi Y (1976) Sequential GMDH algorithm and its application to river flow prediction. IEEE Trans Syst Man Cybernet 7:473–479

    Article  Google Scholar 

  • Imrie CE, Durucan S, Korre A (2000) River flow prediction using artificial neural networks: generalisation beyond the calibration range. J Hydrol 233(1–4):138–153

    Article  Google Scholar 

  • Keskin ME, Taylan D, Terzi O (2006) Adaptive neural-based fuzzy inference system (ANFIS) approach for modelling hydrological time series. Hydrol Sci J 51(4):588–598

    Article  Google Scholar 

  • Kisi O, Genc O, Dinc S, Zounemat-Kermani M (2016) Daily pan evaporation modeling using chi-squared automatic interaction detector, neural networks, classification and regression tree. Comput Electron Agric 122:112–117

    Article  Google Scholar 

  • Li Y, Khan MYA, Jiang Y, Tian F, Liao W, Fu S, He C (2019) CART and PSO+ KNN algorithms to estimate the impact of water level change on water quality in Poyang Lake, China. Arab J Geosci 12(9):287

    Article  Google Scholar 

  • Mahdavi-Meymand A, Scholz M, Zounemat-Kermani M (2019) Challenging soft computing optimization approaches in modeling complex hydraulic phenomenon of aeration process. ISH J Hydraulic Eng:1–12

  • Maier HR, Dandy GC (2000) Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications. Environ Model Softw 15(1):101–124

    Article  Google Scholar 

  • Marsh TJ (2004) The January 2003 flood on the Thames. Weather 59(3):59–62

    Article  Google Scholar 

  • Najafzadeh M, Barani GA, Hessami Kermani MR (2014) Estimation of pipeline scour due to waves by GMDH. J Pipeline Syst Eng Pract 5(3):06014002

    Article  Google Scholar 

  • Nash JE, Sutcliffe JV (1970) River forecasting using conceptual models: part 1-a discussion of principles. J Hydrol 10(3):280–290

    Article  Google Scholar 

  • Niu WJ, Feng ZK (2021) Evaluating the performances of several artificial intelligence methods in forecasting daily streamflow time series for sustainable water resources management. Sustain Cities Soc 64:102562

    Article  Google Scholar 

  • Papacharalampous GA, Tyralis H, Koutsoyiannis D (2019) Comparison of stochastic and machine learning methods for multi-step ahead forecasting of hydrological processes. Stoch Env Res Risk A 33(2):481–514

    Article  Google Scholar 

  • Quilty J, Adamowski J (2018) Addressing the incorrect usage of wavelet-based hydrological and water resources forecasting models for real-world applications with best practices and a new forecasting framework. J Hydrol 563:336–353

    Article  Google Scholar 

  • Rajaee T, Nourani V, Zounemat-Kermani M, Kisi O (2010a) River suspended sediment load prediction: application of ANN and wavelet conjunction model. J Hydrol Eng 16(8):613–627

    Article  Google Scholar 

  • Rajaee T, Shahabi A (2016) Evaluation of wavelet-GEP and wavelet-ANN hybrid models for prediction of total nitrogen concentration in coastal marine waters. Arab J Geosci 9(3):176

    Article  Google Scholar 

  • Sak H, Senior A, Beaufays F (2014) Long short-term memory recurrent neural network architectures for large scale acoustic modeling. In: Fifteenth annual conference of the international speech communication association

    Google Scholar 

  • Santos CAG, Kisi O, da Silva RM, Zounemat-Kermani M (2018) Wavelet-based variability on streamflow at 40-year timescale in the Black Sea region of Turkey. Arab J Geosci 11(8):169

    Article  Google Scholar 

  • Saraiva SV, de Oliveira Carvalho F, Santos CAG, Barreto LC, Freire PKDMM (2021) Daily streamflow forecasting in Sobradinho reservoir using machine learning models coupled with wavelet transform and bootstrapping. Appl Soft Comput 102:107081

    Article  Google Scholar 

  • Schaller N, Kay AL, Lamb R, Massey NR, Van Oldenborgh GJ, Otto FE et al (2016) Human influence on climate in the 2014 southern England winter floods and their impacts. Nat Clim Chang 6(6):627–634

    Article  Google Scholar 

  • Shahriar MDS, Kamruzzaman M, McCulloch J, Beecham S (2016) Multiple step ahead river flow modelling for south east tasmanian aquaculture. Earth Sci Inf 9:271–279

    Article  Google Scholar 

  • Shoaib M, Shamseldin AY, Khan S, Khan MM, Khan ZM, Sultan T, Melville BW (2018) A comparative study of various hybrid wavelet feedforward neural network models for runoff forecasting. Water Resour Manag 32(1):83–103

    Article  Google Scholar 

  • Srinivas VV, Srinivasan KJWRR (2001) A hybrid stochastic model for multiseason streamflow simulation. Water Resour Res 37(10):2537–2549

    Article  Google Scholar 

  • Tsoukalas I, Papalexiou S, Efstratiadis A, Makropoulos C (2018) A cautionary note on the reproduction of dependencies through linear stochastic models with non-gaussian white noise. Water 10(6):771

    Article  Google Scholar 

  • Tyralis H, Papacharalampous G, Langousis A (2019) Super learning for daily streamflow forecasting: large-scale demonstration and comparison with multiple machine learning algorithms. arXiv preprint arXiv:1909.04131

    Google Scholar 

  • Vieira AC, Garcia G, Pabón RE, Cota LP, de Souza P, Ueyama J, Pessin G (2020) Improving flood forecasting through feature selection by a genetic algorithm–experiments based on real data from an Amazon rainforest river. Earth Sci Inform:1–14

  • Wang L, Kisi O, Zounemat-Kermani M, Zhu Z, Gong W, Niu Z, Liu H, Liu Z (2017) Prediction of solar radiation in China using different adaptive neuro-fuzzy methods and M5 model tree. Int J Climatol 37(3):1141–1155

    Article  Google Scholar 

  • Yaseen ZM, El-Shafie A, Afan HA, Hameed M, Mohtar WHMW, Hussain A (2016) RBFNN versus FFNN for daily river flow forecasting at Johor River, Malaysia. Neural Comput Appl 27(6):1533–1542

  • Yaseen ZM, Ebtehaj I, Bonakdari H, Deo RC, Mehr AD, Mohtar WHMW et al (2017) Novel approach for streamflow forecasting using a hybrid ANFIS-FFA model. J Hydrol 554:263–276

    Article  Google Scholar 

  • Zadeh MR, Amin S, Khalili D, Singh VP (2010) Daily outflow prediction by multi layer perceptron with logistic sigmoid and tangent sigmoid activation functions. Water Resour Manag 24(11):2673–2688

    Article  Google Scholar 

  • Zounemat-Kermani M (2012) Hourly predictive Levenberg–Marquardt ANN and multi linear regression models for predicting of dew point temperature. Meteorog Atmos Phys 117(3–4):181–192

    Article  Google Scholar 

  • Zounemat-Kermani M (2014) Principal component analysis (PCA) for estimating chlorophyll concentration using forward and generalized regression neural networks. Appl Artif Intell 28(1):16–29

    Article  Google Scholar 

  • Zounemat-Kermani M, Mahdavi-Meymand A (2019) Hybrid meta-heuristics artificial intelligence models in simulating discharge passing the piano key weirs. J Hydrol 569:12–21

    Article  Google Scholar 

  • Zounemat-Kermani M, Kisi O, Rajaee T (2013) Performance of radial basis and LM-feed forward artificial neural networks for predicting daily watershed runoff. Appl Soft Comput 13(12):4633–4644

  • Zounemat-Kermani M, Matta E, Cominola A, Xia X, Zhang Q, Liang Q, Hinkelmann R (2020) Neurocomputing in surface water hydrology and hydraulics: a review of two decades retrospective, current status and future prospects. J Hydrol 125085

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Acknowledgments

The first author would like to express his gratitude to the Alexander von Humboldt Foundation for providing financial support for this research project within the framework of the Return Fellowship program.

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Authors and Affiliations

  1. Department of Water Engineering, Shahid Bahonar University of Kerman, Kerman, Iran

    Mohammad Zounemat-Kermani

  2. Institute of Hydro-Engineering, Polish Academy of Sciences, Gdańsk, Poland

    Amin Mahdavi-Meymand

  3. Chair of Water Resources Management and Modeling of Hydrosystems, Institute of Civil Engineering, Technische Universität Berlin, Berlin, Germany

    Reinhard Hinkelmann

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  1. Mohammad Zounemat-Kermani
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Correspondence to Mohammad Zounemat-Kermani.

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Zounemat-Kermani, M., Mahdavi-Meymand, A. & Hinkelmann, R. A comprehensive survey on conventional and modern neural networks: application to river flow forecasting. Earth Sci Inform 14, 893–911 (2021). https://doi.org/10.1007/s12145-021-00599-1

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