Prediction of Flood in Barak River Using Hybrid Machine Learning Approaches: A Case Study
Authors
-
Department of Civil Engineering, National Institute of Technology Silchar - 788 010
Abinash Sahoo -
Department of Civil Engineering, National Institute of Technology Silchar - 788 010
Sandeep Samantaray
-
Department of Civil Engineering, National Institute of Technology Silchar - 788 010
Dillip K. Ghose
DOI:
https://doi.org/10.1007/s12594-021-1650-1Keywords:
No Keywords.Abstract
Flooding causes several threats with outcomes which include peril to human and animal life, damage to property, and adversity to agricultural fields. Therefore, flood prediction is of prime importance for reducing loss of life and devastation to property. To model complex nature of hydrologic processes artificial neural network (ANN) tool is effectively being utilized for modelling different nonlinear relationships, and has proved to be an appropriate method for flood prediction. Present study investigated relative accuracy of radial basis function neural network (RBFNN) and support vector machine (SVM) models combined with Firefly Algorithm (FA) in predicting river flood discharge and contrasted with that of regular ANN, RBFNN and SVM models. Monthly river flow data of Silchar and Dholai stations located in Cachar district of Assam, India are utilized for the present study. For assessing model performance, coefficient of determination (R2), mean square error (MSE) and root mean square error (RMSE) were measured. Evaluation of outcomes shows that both RBF-FA (radial basis function - firefly algorithm) and SVM-FA (support vector machine - firefly algorithm) hybrid models give more precise forecasting results than RBFNN, FFBPNN (feed forward back propagation neural network) and SVM models. Yet, it can be observed that SVM-FA model give better forecasting outputs with R 2 value0.9818than RBF-FA model. Results also reveal that simple SVM model performs marginally better than simple ANN model.
Downloads
Metrics
Issue
Section
Downloads
Published
How to Cite
References
Adnan, R., Samad, A.M., Tajjudin, M. and Ruslan, F.A. (2015) Modeling of flood water level prediction using improved RBFNN structure. IEEE international conference on control system, computing and engineering (ICCSCE), pp. 552-556.
Ahmed, A.M. and Shah, S.M.A. (2017) Application of artificial neural networks to predict peak flow of Surma River in Sylhet Zone of Bangladesh. Internat. Jour. Water, v.11(4), pp.363-375.
Awchi, T.A. (2014) River discharges forecasting in northern Iraq using different ANN techniques. Water Resour. Managmt., v.28(3), pp.801-814.
Bafitlhile, T.M. and Li, Z. (2019) Applicability of å-support vector machine and artificial neural network for flood forecasting in humid, Semi-Humid and Semi-Arid Basins in China. Water, v.11(1), p.85.
Billa, L., Shattri, M., Mahmud, A.R. and Ghazali, A.H. (2006) Comprehensive planning and the role of SDSS in flood disaster management in Malaysia. Disaster Prev. Managmt., v.15, pp.233–240.
Broomhead, D.S. and Lowe, D. (1988) Radial basis functions, multivariable functional interpolation and adaptive networks (No. RSRE-MEMO-4148). Royal Signals and Radar Establishment Malvern, United Kingdom.
Buahin, C.A. and Horsburgh, J.S. (2015) Evaluating the simulation times and mass balance errors of component-based models: an application of Open MI 2.0 to an urban stormwater system. Environ. Model. Softw., v.72 (Supplement C), pp.92–109.
Chang, F.J., Liang, J.M. and Chen, Y.C. (2001) Flood forecasting using radial basis function neural networks. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), v.31(4), pp.530-535.
ChePa, N., Hashim, N.L., Yusof, Y. and Hussain, A. (2016) The application of Firefly algorithm in an adaptive emergency evacuation centre management (AEECM) for dynamic relocation of flood victims. In: AIP Conf. Proc., v.1761(1), pp.020034. AIP Publishing LLC.
Ch, S. Sohani, S.K. Kumar, D. Malik, A., Chahar, B.R. Nema, A.K. Panigrahi, B.K. and Dhiman, R.C. (2014) A support vector machine-firefly algorithm based forecasting model to determine malaria transmission, Neurocomputing, v.129, pp.279–288.
Darbandi, S. and Pourhosseini, F.A. (2018) River flow simulation using a multilayer perceptron-firefly algorithm model. Applied Water Science, v.8(3), p.85.
Elsafi, S. H. (2014) Artificial Neural Networks (ANNs) for flood forecasting at Dongola Station in the River Nile, Sudan. Alexandria Engg. Jour., v.53(3), pp.655–662.
Faruq, A., Abdullah, S.S., Marto, A., Bakar, M.A.A., Hussein, S.F.M. and Razali, C.M.C. (2019) The use of radial basis function and non-linear autoregressive exogenous neural networks to forecast multi-step ahead of time flood water level. International Journal of Advances in Intelligent Informatics, v.5(1), pp.1-10.
Fister, I. Fister Jr., X.S. Yang, J. and Brest, A. (2013) Comprehensive review of firefly algorithms, Swarm Evolution. Comput. v.13, pp.34–46.
Gereon, A. (2018) Hands-on Machine Learning with Scikit-Learn and Tensor Flow. O’ Reily Media Inc., USA.
Gharbi, M., Soualmia, A., Dartus, D. and Masbernat, L. (2016) Comparison of 1D and 2D hydraulic models for floods simulation on the Medjerda Riverin Tunisia. Jour. Mater. Environ. Sci., v.7, pp.3017–3026.
Ghorbani, M.A., Zadeh, H.A., Isazadeh, M. and Terzi, O. (2016) A comparative study of artificial neural network (MLP, RBF) and support vector machine models for river flow prediction. Environ. Earth Sci., v.75(6), p.476.
Ghumman, A. R., Ghazaw, Y. M., Sohail, A. R. and Watanabe, K. (2011) Runoff forecasting by artificial neural network and conventional model. Alexandria Engg. Jour., v.50(4), pp.345–350.
Hammid, A.T., Sulaiman, M.H.B. and Awad, O.I. (2018) A robust firefly algorithm with backpropagation neural networks for solving hydrogeneration prediction. Electrical Engg., v.100(4), pp.2617-2633.
Hipni, A., El-shafie, A., Najah, A., Karim, O.A., Hussain, A. and Mukhlisin, M. (2013) Daily forecasting of dam water levels: comparing a support vector machine (SVM) model with adaptive neuro fuzzy inference system (ANFIS). Water Resourc. Managmt, v.27(10), pp.3803-3823.
Hong, H., Tsangaratos, P., Ilia, I., Liu, J., Zhu, A.X. and Chen, W. (2018) Application of fuzzy weight of evidence and data mining techniques in construction of flood susceptibility map of Poyang County, China. Sci. Total Environ., v.625, pp.575-588.
Hu, X., Hall, J.W. and Thacker, S. (2014) Too big to fail? The spatial vulnerability of the Chinese infrastructure system to flooding risks. Vulnerability, Uncertainty and Risk, pp.704–714.
Huang, S., Chang, J., Huang, Q. and Chen, Y. (2014) Monthly streamflow prediction using modified EMD-based support vector machine. Jour. Hydrol., v.511, pp.764-775.
Kartika, N.K.E., Murti, M.A. and Setianingsih, C. (2019) Floods Prediction Using Radial Basis Function (RBF) Based on Internet of Things (IoT). IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT), pp. 125-128.
Khatibi, R., Ghorbani, M.A. and Pourhosseini, F.A. (2017) Stream flow predictions using nature-inspired Firefly Algorithms and a Multiple Model strategy–Directions of innovation towards next generation practices. Advanced Engg. Informatics, v.34, pp.80-89.
Kisi, O., Nia, A.M., Gosheh, M.G., Tajabadi, M.R.J. and Ahmadi, A. (2012) Intermittent streamflow forecasting by using several data driven techniques. Water Resourc. Managmt., v.26(2), pp.457-474.
Kisi, O., Shiri, J., Karimi, S., Shamshirband, S., Motamedi, S., Petkoviæ, D. and Hashim, R. (2015) A survey of water level fluctuation predicting in Urmia Lake using support vector machine with firefly algorithm. Applied Mathematics and Computation, v.270, pp.731-743.
Kisi, O. and Cimen, M. (2011) A wavelet-support vector machine conjunction model for monthly streamflow forecasting. Jour. Hydrol., v.399(1-2), pp.132-140.
Li, S., Ma, K., Jin, Z. and Zhu, Y. (2016) A new flood forecasting model based on SVM and boosting learning algorithms. In 2016 IEEE Congress on Evolutionary Computation (CEC), pp.1343-1348.
Mazzoleni, M., Bacchi, B., Barontini, S., Di Baldassarre, G., Pilotti, M. and Ranzi, R. (2014) Flooding hazard mapping in floodplain areas affected by piping breaches in the Po River, Italy. Jour. Hydrol. Eng., v.19, pp.717–731.
Mehr, A.D., Nourani, V., Khosrowshahi, V.K. and Ghorbani, M.A. (2019) A hybrid support vector regression–firefly model for monthly rainfall forecasting. Internat. Jour. Environ. Sci. Tech., v.16(1), pp.335-346.
Mohamadi, S., Ehteram, M. and El-Shafie, A. (2020) Accuracy enhancement for monthly evaporation predicting model utilizing evolutionary machine learning methods. Internat. Jour. Environ. Sci. Tech., pp.1-24.
Moradkhani, H., Hsu, K., Gupta, H.V. and Sorooshian, S. (2004) Improved streamflow forecasting using self-organizing radial basis function artificial neural networks. Jour. Hydrol., v.295, pp.246–262.
Pal, S.K., Rai, C.S. and Singh, A.P. (2012) Comparative study of firefly algorithm and particle swarm optimization for noisy non-linear optimization problems. Internat. Jour. Intelligent systems and applications, v.4(10), pp.50.
Ruslan, F.A., Samad, A.M., Zain, Z.M. and Adnan, R. (2013) Modelling flood prediction using Radial Basis Function Neural Network (RBFNN) and inverse model: a comparative study. In 2013 IEEE International Conference on Control System, Computing and Engineering, pp. 577-581.
Sanubari, A.R., Kusuma, P.D. and Setianingsih, C. (2018) November. Flood Modelling and Prediction Using Artificial Neural Network. IEEE International Conference on Internet of Things and Intelligence System (IOTAIS), pp. 227-233.
Samantaray, S. and Sahoo, A. (2020) Estimation of runoff through BPNN and SVM in Agalpur Watershed. In Frontiers in Intelligent Computing: Theory and Applications (pp. 268-275). Springer, Singapore.
Samantaray, S. and Sahoo, A. (2020) Appraisal of runoff through BPNN, RNN, and RBFN in Tentulikhunti Watershed: a case study. In Frontiers in Intelligent Computing: Theory and Applications (pp. 258-267). Springer, Singapore.
Sahoo, A., Samantaray, S. and Ghose, D.K. (2019) Stream Flow Forecasting in Mahanadi River Basin using Artificial Neural Networks. Procedia Computer Science, v.157, pp.168-174.
Samantaray, S. and Sahoo, A. (2020) Assessment of sediment concentration through RBNN and SVM-FFA in Arid Watershed, India. In Smart Intelligent Computing and Applications (pp.701-709). Springer, Singapore.
Samantaray, S., Sahoo, A. and Ghose, D.K. (2019) Assessment of runoff via precipitation using neural networks: watershed modelling for developing environment in arid region. Pertanika Jour. Sci. Tech., v.27(4), pp.2245-2263.
Sankaranarayanan, S., Prabhakar, M., Satish, S., Jain, P., Ramprasad, A. and Krishnan, A. (2019) Flood prediction based on weather parameters using deep learning. Jour. Water and Climate Change, DOI:10.2166/wcc.2019. 321
Shrestha, N.K., Leta, O.T., De Fraine, B., van Griensven, A. and Bauwens, W. (2013) Open MI based integrated sediment transport modelling of the river Zenne, Belgium. Environ. Model. Softw., v.47, pp.193–206.
Soleymani, S.A., Goudarzi, S., Anisi, M.H., Hassan, W.H., Idris, M.Y.I., Shamshirband, S., Noor, N.M. and Ahmedy, I. (2016) A novel method to water level prediction using RBF and FFA. Water Resourc. Managmt., v.30(9), pp.3265-3283.
Tehrany, M.S., Pradhan, B., Mansor, S. and Ahmad, N. (2015) Flood susceptibility assessment using GIS-based support vector machine model with different kernel types. Catena, v.125, pp.91-101.
Tehrany, M.S., Pradhan, B. and Jebur, M.N., (2015) Flood susceptibility analysis and its verification using a novel ensemble support vector machine and frequency ratio method. Stochastic Environmental Research and Risk Assessment, v.29(4), pp.1149-1165.
Vapnik, V.N. (1998) Statistical learning theory. Wiley, New York, p.736.
Yang, X.S. (2013) Multiobjective firefly algorithm for continuous optimization, Engg. Comp., v.29 (2), pp.175–184.
Wilde PD (1997) Neural network models: theory and projects, 2nd edn. Springer, London
Zhao, G., Pang, B., Xu, Z., Peng, D. and Xu, L. (2019) Assessment of urban flood susceptibility using semi-supervised machine learning model. Sci. Total Environ., v.659, pp.940-949.
Zhou, S.L., McMahon, T.A, Walton, A. and Lewis, J. (2002) Forecasting operational demand for an urban water supply zone. Jour Hydrol., v.259, pp.189–202.
Zounemat-Kermani, M., Kisi, O. and Rajaee, T. (2013) Performance of radial basis and LM-feed forward artificial neural networks for predicting daily watershed runoff. Applied Soft Computing, v.13(12), pp.4633-4644.
