Abstract
The water level measurements in a river is important for a variety of reasons. Because of its significant impacts on various aspects, accurate water level prediction is important in river management. Three data-driven water level forecasting models are analyzed and presented. One is based on Box-Jenkins approach, while the other two are based on the ANN and SVM approaches, respectively. The analysis is made with great attention to the reliability and accuracy of each model, with reference to daily water level data of Sungai Kemaman at Jambatan Air Putih, Terengganu. The aim of this study is to propose the best model that suitable and appropriate for predicting the water level. The experimental results revealed that model SVM (2) gives the best comparative value which indicate that this model was suitable to predict the daily water level. The values of SVM (2) are MAE = 8.8453, MSE = 0.1574, RMSE = 0.3968, r = 0.8667 and CE = 0.9979. The results of this study demonstrated the suggested model’s capability in prediction of water level given the characteristics of the data which appear to be not stationary, not normally distributed and not linear. The suggested SVM (2) presents a potential alternative approach for prediction water level data, as evidenced by this research.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Arbain, S.H., Wibowo, A.: Time series methods for water level forecasting of Dungun river in Terengganu Malaysia. Int. J. Eng. Sci. Technol. 4, 1803–1811 (2012)
Khan, M.Y.A., Hasan, F., Panwar, S., Chakrapani, G.J.: Neural network model for discharge and water-level prediction for Ramganga River catchment of Ganga Basin, India. Hydrol. Sci. J. 61, 2084–2095 (2016)
Panyadee, P., Champrasert, P., Aryupong, C.: Water level prediction using artificial neural network with particle swarm optimization model. In: 2017 5th International Conference on Information and Communication Technology, ICoIC7 2017, pp. 3–8 (2017)
Alvisi, S., Mascellani, G., Franchini, M., Bárdossy, A.: Water level forecasting through fuzzy logic and artificial neural network approaches. Hydrol. Earth Syst. Sci. 10, 1–17 (2006)
Vahdat, S.F., Sarraf, A., Shamsnia, A.: Prediction of monthly mean Inflow to Dez Dam reservoir using time series models (Box-Jenkins). In: 2011 International Conference on Environment and Industrial Innovation IPCBEE, vol. 12, pp. 162–166 (2011)
Zhu, S., Lu, H., Ptak, M., Dai, J., Ji, Q.: Lake water-level fluctuation forecasting using machine learning models: a systematic review. Environ. Sci. Pollut. Res. 27(36), 44807–44819 (2020)
Yu, Z., Lei, G., Jiang, Z., Liu, F.: ARIMA modelling and forecasting of water level in the middle reach of the Yangtze River. In: 2017 4th International Conference on Transportation Information and Safety, ICTIS 2017 – Proceedings, pp. 172–177 (2017)
Phan, T.T.H., Nguyen, X.H.: Combining statistical machine learning models with ARIMA for water level forecasting: the case of the Red river. Adv. Water Resour. 142(June), 103656 (2020)
Adnan, R., Ruslan, F.A., Samad, A.M., Md. Zain, Z.: Flood water level modelling and prediction using artificial neural network: Case study of Sungai Batu Pahat in Johor. In: Proceedings - 2012 IEEE Control and System Graduate Research Colloquium, ICSGRC 2012 (2012)
Tripathy, N.: Forecasting gold price with auto regressive integrated moving average model. Int. J. Econ. Financ. Issues 7(4), 324–329 (2017)
Taormina, R., Chau, K.W.: ANN-based interval forecasting of streamflow discharges using the LUBE method and MOFIPS. Eng. Appl. Artif. Intell. 45, 429–440 (2015)
Abbot, J., Marohasy, J.: Input selection and optimisation for monthly rainfall forecasting in Queensland, Australia, using artificial neural networks. Atmos. Res. 138, 166–178 (2014)
Seo, I.W., Yun, S.H., Choi, S.Y.: Forecasting water quality parameters by ANN model using pre-processing technique at the downstream of Cheongpyeong Dam. Proc. Eng. 154, 1110–1115 (2016)
El-Mahdy, M.E.-S., El-Abd, W.A., Morsi, F.I.: Forecasting lake evaporation under a changing climate with an integrated artificial neural network model: a case study Lake Nasser, Egypt. J. Afr. Earth Sci. 176(June 2020), 104191 (2021)
Londhe, S.N.: Water levels forecasting using artificial neural networks. Int. J. Ocean Clim. Syst. 2(2), 119–135 (2011)
Okasha, M.K.: using support vector machines in financial time series forecasting. Int. J. Stat. Appl. 4(1), 28–39 (2014)
Kim, K.J.: Financial time series forecasting using support vector machines. Neurocomputing 55(1–2), 307–319 (2003)
Hipni, A., El-shafie, A., Najah, A., Karim, O.A., Hussain, A., Mukhlisin, M.: Daily forecasting of dam water levels: comparing a support vector machine (SVM) model with adaptive neuro fuzzy inference system (ANFIS). Water Resour. Manag. 27(10), 3803–3823 (2013)
Gao, C., Bompard, E., Napoli, R., Cheng, H.: Price forecast in the competitive electricity market by support vector machine. Phys. A: Stat. Mech. Appl. 382(1), 98–113 (2007)
Bray, M., Han, D.: Identification of support vector machines for runoff modelling. J. Hydroinform. 6(4), 265–280 (2004)
Taian, L., Xin, X., Xinying, L., Huiqi, Z.: Application research of support vector regression in coal mine ground-water-level forecasting. In: Proceedings - 2009 International Forum on Information Technology and Applications, IFITA 2009, vol. 2, no. 7, pp. 507–509 (2009)
Rezaie-Balf, M., Kisi, O.: New formulation for forecasting streamflow: Evolutionary polynomial regression vs. extreme learning machine. Hydrol. Res. 49(3), 939–953 (2018)
Kişi, Ö.: Streamflow forecasting using different artificial neural network algorithms. J. Hydrol. Eng. 12(5), 532–539 (2007)
Adnan, R.M., Petroselli, A., Heddam, S., Santos, C.A.G., Kisi, O.: Short term rainfall-runoff modelling using several machine learning methods and a conceptual event-based model. Stochast. Environ. Res. Risk Assess. 35(3), 597–616 (2021)
Acknowledgments
The authors would like to thank the Ministry of Higher Education Malaysia (MOHE) for supporting this research under Fundamental Research Grant Scheme Vot No. FRGS/1/2018/STG06/UTHM/03/3 and partially sponsor by Universiti Tun Hussein Onn Malaysia under Multi-Displinary Grant Vot No. H508.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Noorain, I.S., Ismail, S., Sadon, A.N., Yasin, S.M. (2022). Application of Box-Jenkins, Artificial Neural Network and Support Vector Machine Model for Water Level Prediction. In: Ghazali, R., Mohd Nawi, N., Deris, M.M., Abawajy, J.H., Arbaiy, N. (eds) Recent Advances in Soft Computing and Data Mining. SCDM 2022. Lecture Notes in Networks and Systems, vol 457. Springer, Cham. https://doi.org/10.1007/978-3-031-00828-3_12
Download citation
DOI: https://doi.org/10.1007/978-3-031-00828-3_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-00827-6
Online ISBN: 978-3-031-00828-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)