Daily streamflow forecasting by machine learning in Tra Khuc river in Vietnam
Author affiliations
DOI:
https://doi.org/10.15625/2615-9783/17914Keywords:
machine learning, streamflow, gate recurrent unit, bacterial foraging optimization, gray wolf optimizer, human group optimizationAbstract
Precise streamflow prediction is crucial in the optimization of the distribution of water resources. This study develops the machine learning models by integrating recurrent gate unit (GRU) with bacterial foraging optimization (BFO), gray wolf optimizer (GWO), and human group optimization (HGO) to forecast the streamflow in the Tra Khuc River, Vietnam. For this purpose, the time series of daily rainfall and river flow at Son Giang station from 2000 to 2020 were employed to forecast the streamflow. The statistical indices, namely the root mean square error, the mean absolute error, and the coefficient of determination (R²), was utilized to evaluate the performance of the proposed models. The results showed that the three optimization algorithms (HGO, GWO, and BFO) effectively enhanced the performance of the GRU model.
Moreover, among the four models (GRU, GRU-HGO, GRU-GWO, and GRU-BFO), the GRU-GWO model outperformed the other models with R² = 0.883. GRU-HGO achieved R² = 0.879, and GRU-BFO achieved R²=0.878. The results of this study showed that GRU combined with optimization algorithms is a reliable modeling approach in short-term flow forecasting.
Downloads
References
Abdel-Basset M., Abdel-Fatah L., Sangaiah A.K., 2018. Metaheuristic algorithms: A comprehensive review. Computational intelligence for multimedia big data on the cloud with engineering applications, 185-231.
Adnan R.M., Liang Z., Trajkovic S., Zounemat-Kermani M., Li B., Kisi O., 2019. Daily streamflow prediction using optimally pruned extreme learning machine. Journal of Hydrology, 577, 123981.
Adnan R.M., Mostafa R.R., Elbeltagi A., Yaseen Z.M., Shahid S., Kisi O., 2022. Development of new machine learning model for streamflow prediction: Case studies in Pakistan. Stochastic Environmental Research and Risk Assessment, 36(4), 999-1033.
Adnan R.M., Mostafa R.R., Kisi O., Yaseen Z.M., Shahid S., Zounemat-Kermani M., 2021. Improving streamflow prediction using a new hybrid ELM model combined with hybrid particle swarm optimization and grey wolf optimization. Knowledge-Based Systems, 230, 107379.
Adnan R.M., Kisi O., Mostafa R.R., Ahmed A.N., El-Shafie A., 2021. The potential of a novel support vector machine trained with modified mayfly optimization algorithm for streamflow prediction. Hydrological Sciences Journal, 67(2), 161-174.
Adnan R.M., Liang Z., Heddam S., Zounemat-Kermani M., Kisi O., Li B., 2020. Least square support vector machine and multivariate adaptive regression splines for streamflow prediction in mountainous basin using hydro-meteorological data as inputs. Journal of Hydrology, 586, 124371.
Ahmed A.M., Deo R.C., Feng Q., Ghahramani A., Raj N., Yin Z., Yang L., 2021. Deep learning hybrid model with Boruta-Random forest optimiser algorithm for streamflow forecasting with climate mode indices, rainfall, and periodicity. Journal of Hydrology, 599, 126350.
Alizadeh B., Bafti A.G., Kamangir H., Zhang Y., Wright B.D., Franz K. J., 2021. A novel attention-based LSTM cell post-processor coupled with bayesian optimization for streamflow prediction. Journal of Hydrology, 601, 126526.
Bieger K., Hörmann G., Fohrer N., 2014. Simulation of streamflow and sediment with the soil and water assessment tool in a data scarce catchment in the three Gorges region, China. Journal of Environmental Quality, 43(1), 37-45.
Brezočnik L., Fister I., Podgorelec V., 2018. Swarm intelligence algorithms for feature selection: a review. Applied Sciences, 8(9), 1521.
Bui Q.T., Nguyen Q.H., Nguyen X.L., Pham V.D., Nguyen H.D., Pham V.M., 2020. Verification of novel integrations of swarm intelligence algorithms into deep learning neural network for flood susceptibility mapping. Journal of Hydrology, 581, 124379.
Carbone G., I. Giannoccaro, 2015. Model of human collective decision-making in complex environments. The European Physical Journal, B88(12), 1-10.
Ch S., Anand N., Panigrahi B.K., Mathur S., 2013. Streamflow forecasting by SVM with quantum behaved particle swarm optimization. Neurocomputing, 101, 18-23.
Chakraborty A., A.K. Kar, 2017. Swarm intelligence: A review of algorithms. Nature-inspired computing and optimization, 475-494.
Cheng M., Fang F., Kinouchi T., Navon I., Pain C., 2020. Long lead-time daily and monthly streamflow forecasting using machine learning methods. Journal of Hydrology, 590, 125376.
Cho K., Y. Kim, 2022. Improving streamflow prediction in the WRF-Hydro model with LSTM networks. Journal of Hydrology, 605, 127297.
Cho K., Van Merriënboer B., Gulcehre C., Bahdanau D., Bougares F., Schwenk H., Bengio Y., 2014. Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv, 1406.1078.
De Vincenzo I., Giannoccaro I., Carbone G., 2016. The Human Group Optimizer (HGO): Mimicking the collective intelligence of human groups as an optimization tool for combinatorial problems. Preprint at https://arxiv.org/abs/1608.01495.
De Vincenzo I., Giannoccaro I., Carbone G., 2016. The Human Group Optimizer (HGO): Mimicking the collective intelligence of human groups as an optimization tool for combinatorial problems arXiv preprint arXiv, 1608.01495.
Dehghani R., Torabi Poudeh H., Younesi H., Shahinejad B., 2020. Daily streamflow prediction using support vector machine-artificial flora (SVM-AF) hybrid model. Acta Geophysica, 68(6), 1763-1778.
DiMaggio P.J., W.W. Powell, 1983. The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields. American sociological review, 147-160.
Dolling O.R., E.A. Varas, 2002. Artificial neural networks for streamflow prediction. Journal of Hydraulic Research, 40(5), 547-554.
Easton Z.M., Fuka D.R., Walter M.T., Cowan D.M., Schneiderman E.M., Steenhuis T.S., 2008. Re-conceptualizing the soil and water assessment tool (SWAT) model to predict runoff from variable source areas. Journal of Hydrology, 348(3-4), 279-291.
Ghimire S., Yaseen Z.M., Farooque A.A., Deo R.C., Zhang J., Tao X., 2021. Streamflow prediction using an integrated methodology based on convolutional neural network and long short-term memory networks. Scientific Reports, 11(1), 1-26.
Ghosh A., Roy M.B., Roy P.K., 2022. Evaluating the performance of MIKE NAM model on rainfall-runoff in lower Gangetic floodplain, West Bengal, India. Modeling Earth Systems and Environment, 1-17.
Gumindoga W., Rientjes T., Haile A., Dube T., 2014. Predicting streamflow for land cover changes in the Upper Gilgel Abay River Basin, Ethiopia: A TOPMODEL based approach. Physics and Chemistry of the Earth, Parts A/B/C, 76, 3-15.
Gumindoga W., Rwasoka D., Murwira A., 2011. Simulation of streamflow using TOPMODEL in the Upper Save River catchment of Zimbabwe. Physics and Chemistry of the Earth, Parts A/B/C, 36(14-15), 806-813.
Ha S., Liu D., Mu L., 2021. Prediction of Yangtze River streamflow based on deep learning neural network with El Niño-Southern Oscillation. Scientific Reports, 11(1), 1-23.
Hao P., B. Sobhani, 2021. Application of the improved chaotic grey wolf optimization algorithm as a novel and efficient method for parameter estimation of solid oxide fuel cells model. International Journal of Hydrogen Energy, 46(73), 36454-36465.
Hernández-Ocana B., Mezura-Montes E., Pozos-Parra P., 2013. A review of the bacterial foraging algorithm in constrained numerical optimization. In Proceedings of the 2013 IEEE Congress on Evolutionary Computation, Cancun, Mexico, 20-23, 2695-2702.
Hosseinzadeh Talaee P., 2014. Multilayer perceptron with different training algorithms for streamflow forecasting. Neural Computing and Applications, 24(3), 695-703.
Hunt K.M., Matthews G.R., Pappenberger F., Prudhomme C., 2022. Using a long short-term memory (LSTM) neural network to boost river streamflow forecasts over the western United States. Hydrology and Earth System Sciences Discussions, 1-30.
Hussain D., Hussain T., Khan A.A., Naqvi S.A.A., Jamil A., 2020. A deep learning approach for hydrological time-series prediction: A case study of Gilgit river basin. Earth Science Informatics, 13(3), 915-927.
Hussain D., A.A. Khan, 2020. Machine learning techniques for monthly river flow forecasting of Hunza River, Pakistan. Earth Science Informatics, 13(3), 939-949.
Khosravi K., Golkarian A., Booij M.J., Barzegar R., Sun W., Yaseen Z.M., Mosavi A., 2021. Improving daily stochastic streamflow prediction: Comparison of novel hybrid data-mining algorithms. Hydrological Sciences Journal, 66(9), 1457-1474.
Kosravi K., Golkarian A., Tiefenbacher J.P., 2022. Using Optimized Deep Learning to Predict Daily Streamflow: A Comparison to Common Machine Learning Algorithms. Water Resour Manage, 36, 699-716.
Kisi O., M. Cimen, 2011. A wavelet-support vector machine conjunction model for monthly streamflow forecasting. Journal of Hydrology, 399(1-2), 132-140.
Kisi O., Ozkan C., Akay B., 2012. Modeling discharge-sediment relationship using neural networks with artificial bee colony algorithm. Journal of Hydrology, 428, 94-103.
Kilinc H.C., Yurtsever A., 2019. Short-Term Streamflow Forecasting Using Hybrid Deep Learning Model Based on Grey Wolf Algorithm for Hydrological Time Series. Sustainability, 14(6), 3352.
Lane R., Coxon G., Freer J., Wagener T., Johnes P., Bloomfield J., Greene S., Macleod C., Reaney S., 2019. Benchmarking the predictive capability of hydrological models for river flow and flood peak predictions across over 1000 catchments in Great Britain. Hydrology and Earth System Sciences, 23, 4011-4032.
Li Y., Yang J., Wang H., Cui J., Ma Y., Huang S., 2020. Dynamic equivalent modeling for microgrid based on GRU. Energy Reports, 6, 1291-1297.
Luu C., Nguyen D.D., Amiri M., Tran V.P., Bui D.Q., Prakash I., Pham T.B., 2022. Flood susceptibility modeling using Radial Basis Function Classifier and Fisher's linear discriminant function. Science of the Earth, 44(1), 55-72.
Meshram S.G., Meshram C., Santos C.A.G., Benzougagh B., Khedher K.M., 2022. Streamflow prediction based on artificial intelligence techniques. Iranian Journal of Science and Technology, Transactions of Civil Engineering, 46(3), 2393-2403.
Mirjalili S., Mirjalili S.M., Lewis A., 2014. Grey wolf optimizer. Advances in Engineering Software, 69, 46-61.
Muhammad A.U., Li X., Feng J., 2019. Using LSTM GRU and Hybrid Models for Streamflow Forecasting. In International Conference on Machine Learning and Intelligent Communications. Springer, Cham, 510-524.
Mosavi A., Ozturk P., Chau K.-W., 2018. Flood prediction using machine learning models: Literature review. Water, 10(11), 1536.
Nannawo A.S., Lohani T.K., Eshete A.A., Ayana M.T., 2022. Evaluating the dynamics of hydroclimate and streamflow for data-scarce areas using MIKE11-NAM model in Bilate river basin, Ethiopia. Modeling Earth Systems and Environment, 1-16.
Nguyen D.H., Le X.H., Anh D.T., Kim S.-H., Bae D.-H., 2022. Hourly streamflow forecasting using a Bayesian additive regression tree model hybridized with a genetic algorithm. Journal of Hydrology, 606, 127445.
Nguyen H.D., 2022. GIS-based hybrid machine learning for flood susceptibility prediction in the Nhat Le-Kien Giang watershed, Vietnam. Earth Science Informatics, 1-18.
Nguyen H.D., Dang D.K., Nguyen Q.-H., Bui Q.-T., Petrisor A.-I., 2022. Evaluating the effects of climate and land use change on the future flood susceptibility in the central region of Vietnam by integrating land change modeler, machine learning methods. Geocarto International, 1-36.
Nhu V.-H., Bui T.T., My L.N., Vuong H., Duc H.N., 2022. A new approach based on integration of random subspace and C4.5 decision tree learning method for spatial prediction of shallow landslides. Vietnam Journal of Earth Sciences, 44(3), 327-342.
Panahi F., Ehteram M., Ahmed A.N., Huang Y.F., Mosavi A., El-Shafie A., 2021. Streamflow prediction with large climate indices using several hybrid multilayer perceptrons and copula Bayesian model averaging. Ecological Indicators, 133, 108285.
Parisouj P., Mohebzadeh H., Lee T., 2020. Employing machine learning algorithms for streamflow prediction: a case study of four river basins with different climatic zones in the United States. Water Resources Management, 34(13), 4113-4131.
Passino K.M., 2010. Bacterial foraging optimization. International Journal of Swarm Intelligence Research (IJSIR), 1(1), 1-16.
Peng F., Wen J., Zhang Y., Jin J., 2020. Monthly streamflow prediction based on random forest algorithm and phase space reconstruction theory. IOP Journal of Physics: Conference Series, 1637(1), 1-6.
Rahimzad M., Moghaddam Nia A., Zolfonoon H., Soltani J., Danandeh Mehr A., Kwon H.-H., 2021. Performance comparison of an lstm-based deep learning model versus conventional machine learning algorithms for streamflow forecasting. Water Resources Management, 35(12), 4167-4187.
Reis G.B., Silva D.D., Filho E.I.F., Moreira M.C., Veloso G.V., Fraga M.S., Pinheiro S.A.R., 2021. Effect of environmental covariable selection in the hydrological modeling using machine learning models to predict daily streamflow. Journal of Environmental Management, 290, 112625.
Sahib M.A., Abdulnabi A.R., Mohammed M.A., 2018. Improving bacterial foraging algorithm using non-uniform elimination-dispersal probability distribution. Alexandria Engineering Journal, 57(4), 3341-3349.
Samanataray S., A. Sahoo, 2021. A Comparative study on prediction of monthly streamflow using hybrid ANFIS-PSO approaches. KSCE Journal of Civil Engineering, 25(10), 4032-4043.
Saraiva S.V., de Oliveira Carvalho F., Santos C.A.G., Barreto L.C., Freire P.K.D.M.M., 2021. Daily streamflow forecasting in Sobradinho Reservoir using machine learning models coupled with wavelet transform and bootstrapping. Applied Soft Computing, 102, 107081.
Seo Y., Kim S., Kisi O., Singh V.P., 2015. Daily water level forecasting using wavelet decomposition and artificial intelligence techniques. Journal of Hydrology, 520, 224-243.
Sharma P.J., Patel P., Jothiprakash V., 2021. Data-driven modelling framework for streamflow prediction in a physio-climatically heterogeneous river basin. Soft Computing, 25(8), 5951-5978.
Siddiqi T.A., Ashraf S., Khan S.A., Iqbal M.J., 2021. Estimation of data-driven streamflow predicting models using machine learning methods. Arabian Journal of Geosciences, 14(11), 1-9.
Tao L., He X., Wang R., 2017. A hybrid LSSVM model with empirical mode decomposition and differential evolution for forecasting monthly Precipitation. Journal of Hydrometeorology, 18(1), 159-176.
Tesemma Z., Wei Y., Peel M., Western A., 2015. The effect of year-to-year variability of leaf area index on Variable Infiltration Capacity model performance and simulation of runoff. Advances in Water Resources, 83, 310-322.
Tian D., He X., Srivastava P., Kalin P., 2021. A hybrid framework for forecasting monthly reservoir inflow based on machine learning techniques with dynamic climate forecasts, satellite-based data, and climate phenomenon information. Stochastic Environmental Research and Risk Assessment, 1-23.
Tikhamarine Y., Souag-Gamane D., Ahmed A.N., Kisi O., El-Shafie A., 2020. Improving artificial intelligence models accuracy for monthly streamflow forecasting using grey Wolf optimization (GWO) algorithm. Journal of Hydrology, 582, 124435.
Tikhamarine Y., Souag-Gamane D., Kisi O., 2019. A new intelligent method for monthly streamflow prediction: hybrid wavelet support vector regression based on grey wolf optimizer (WSVR-GWO). Arabian Journal of Geosciences, 12(17), 1-20.
Tran V.N., J. Kim, 2022. Robust and efficient uncertainty quantification for extreme events that deviate significantly from the training dataset using polynomial chaos-kriging. Journal of Hydrology, 609, 127716.
Tyralis H., Papacharalampous G., Langousis A., 2021. Super ensemble learning for daily streamflow forecasting: Large-scale demonstration and comparison with multiple machine learning algorithms. Neural Computing and Applications, 33(8), 3053-3068.
Wang J.-S., Li S.-X., 2019. An improved grey wolf optimizer based on differential evolution and elimination mechanism. Scientific Reports, 9(1), 1-21.
Wang J., Cui Q., Sun X., 2021. A novel framework for carbon price prediction using comprehensive feature screening, bidirectional gate recurrent unit and Gaussian process regression. Journal of Cleaner Production, 314, 128024.
Wang K., Shi H., Chen J., Li T., 2019. An improved operation-based reservoir scheme integrated with Variable Infiltration Capacity model for multiyear and multipurpose reservoirs. Journal of Hydrology, 571, 365-375.
Wang X., Xu J., Shi W., Liu J., 2019. OGRU: An optimized gated recurrent unit neural network. Journal of Physics: Conference Series, 1325, 012089.
Zakhrouf M., Hamid B., Kim S., Madani S., 2021. Novel insights for streamflow forecasting based on deep learning models combined the evolutionary optimization algorithm. Physical Geography, 1-24.
Zhang X., Lin Q., Mao W., Liu S., Dou Z., Liu G., 2021. Hybrid Particle Swarm and Grey Wolf Optimizer and its application to clustering optimization. Applied Soft Computing, 101, 107061.
Zhao X., Lv H., Sang Y., Wei Y., Zhu X., 2021. Enhancing robustness of monthly streamflow forecasting model using gated recurrent unit based on improved grey wolf optimizer. Journal of Hydrology, 601, 126607.
Zhao X., Lv H., Wei Y., Lv S., Zhu X., 2021. Streamflow forecasting via two types of predictive structure-based gated recurrent unit models. Water, 13(1), 91.
Downloads
Published
How to Cite
Issue
Section
