Assessment of Flood Frequency using Statistical and Hybrid Neural Network Method: Mahanadi River Basin, India
Authors
-
Department of Civil Engineering, National Institute of Technology, Silchar, Assam
Sandeep Samantaray -
Department of Civil Engineering, National Institute of Technology, Silchar, Assam
Abinash Sahoo
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Department of Civil Engineering, National Institute of Technology, Silchar, Assam
Ankita Agnihotri
DOI:
https://doi.org/10.1007/s12594-021-1785-0Keywords:
No KeywordsAbstract
Flooding is the most common and widespread natural hazard affecting societies around the globe. In this context, forecasting of peak flood discharge is necessary for planning, designing and managing hydraulic structures and is crucial for decision makers to mitigate flooding risks. This study investigates potential of four most frequently used traditional statistical distribution techniques and three neural network algorithms for flood forecasting. Four statistical methods includes Generalized Extreme Value (GEV), Log Pearson-III (LP-III), Gumbel, and Normal. The methods were used for modeling annual maximum discharge at Andhiyarkore, Bamanidhi, Baronda, Kurubhatta gauge station of the river Mahanadi for a period of 60 years (1960 to 2019). In addition, a new hybrid neural network approach (ANFIS-FFA) combining the optimization model i.e. Firefly Algorithm (FFA) with data-driven model Adaptive Neuro Fuzzy Inference System (ANFIS) is adopted to predict flood discharge and compare the obtained results with conventional algorithms. Three statistical constraints MSE, RMSE, WI are employed to find the performance of proposed hybrid model. Result shows that, ANFIS-FFA gives the best values of WI as 0.9604, 0.961, 0.9598 and 0.9615 at Andhiyarkore, Bamanidhi, Baronda, Kurubhatta gauge stations respectively during testing phase. Again regression analysis is done to find the value for coefficient of determination; it gives the best value of R2 as 95.906, 96.014, 96.113, 96.131 at Andhiyarkore, Bamanidhi, Baronda, Kurubhatta gauge stations considering ANFIS-FFA algorithm. Results from this comparative exercise suggest that hybrid ANFIS-FFA gives best performance compared to other statistical and conventional neural network approaches.
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Ang, A.H.S. and Tang, W.H. (2007) Probability Concepts in Engineering Planning: Emphasis on Applications to Civil and Environmental Engineering, John Wiley and Sons.
Anusree, K. and Varghese, K.O. (2016) Streamflow prediction of Karuvannur River Basin using ANFIS, ANN and MNLR models. Procedia Technology, v.24, pp.101-108.
Aqil, M., Kita, I., Yano, A. and Nishiyama, S. (2007) Analysis and prediction of flow from local source in a river basin using a Neuro-fuzzy modeling tool. Jour. Environ. Managmt., v.85, pp.215-223.
Bhagat, N. (2017) Flood Frequency Analysis Using Gumbel's Distribution Method: A Case Study of Lower Mahi Basin, India. Jour. Water Resour. Ocean Sci., v.6, pp.51-54.
Boelee, L., Lumbroso, D.M., Samuels, P.G. and Cloke, H.L. (2019) Estimation of uncertainty in flood forecasts-A comparison of methods. Jour. Flood Risk Managmt., v.12, pp.e12516.
Burke, C.B., Burke, T.T. (2008) Storm Drainage Manual. Indiana LTAP. Busari AO, Mohammed S, Ajibola O. (2013) Evaluation of best fit probability models for the prediction of rainfall and runoff volume (Case Study of Tagwai Dam, Minna-Nigeria). Internat. Jour. Engg. Tech.,. v.3(2), pp.4- 6.
Castillo, R. (1988) Estadistica de valores Extremos. Distribuciones Asintoticas. Estadistica Española, v.29, pp.5-34.
Cheng, M., Fang, F., Kinouchi, T., Navon, I.M. and Pain, C.C. (2020) Long lead-time daily and monthly streamflow forecasting using machine learning methods. Jour. Hydrol., v.590, pp.125376.
Ebtehaj, I., Bonakdari, H. and Gharabaghi, B. (2018) Development of more accurate discharge coefficient prediction equations for rectangular side weirs using adaptive neuro-fuzzy inference system and generalized group method of data handling. Measurement, v.116, pp.473-482.
Elsebaie, I.H. (2012) Developing rainfall intensity-duration-frequency relationship for two regions in Saudi Arabia. Jour. King Saud Univ.-Engg. Sci., v.24, pp.131-140.
Farhoudi, J., Hosseini, S.M. and Sedghi-Asl, M. (2010) Application of neurofuzzy model to estimate the characteristics of local scour downstream of stilling basins. Jour. Hydroinformatics, v.12, pp.201-211.
Günther, F. and Fritsch, S. (2010) neuralnet: Training of neural networks. The R Jour., v.2, pp.30-38.
Haan, C.T. (1977) Statistical Methods in Hydrology, Iowa State University Press, Iowa.
Hornik, K., Stinchcombe, M. and White, H. (1989) Multilayer feed forward networks are universal approximators. Neural Networks, v.2, pp.359-366.
Imrie, C.E., Durucan, S., and Korre, A. (2000) River flow prediction using artificial neural networks: generalisation beyond the calibration range. Jour. Hydrol., v.233(1-4), pp.138-153.
Jang, J.-S. (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE transactions on systems, man, and cybernetics, v.23, pp.665-685.
Kurian, C., Sudheer, K.P., Vema, V.K. and Sahoo, D. (2020) Effective flood forecasting at higher lead times through hybrid modelling framework. Jour. Hydrol., pp.124945.
Li, W., Duan, Q., Miao, C., Ye, A., Gong, W. and Di, Z. (2017) A review on statistical post processing methods for hydrometeorological ensemble forecasting. Wiley Interdisciplinary Reviews: Water, v.4, pp.e1246.
Mamman, M.J., Martins, O.Y., Ibrahim, J. and Shaba, M.I. (2017) Evaluation of Best-Fit Probability Distribution Models for the Prediction of Inflows of Kainji Reservoir, Niger State, Nigeria. Air, Soil Water Res., v.10, pp.1178622117691034.
Meeyaem, K. and Polpinit, P. (2014) Mathematical Model for Flood Forecasting of the Chi River Basin. International Proceedings of Chemical, Biological and Environmental Engineering (IPCBEE), v.63, pp.5-9.
Mesbahzadeh, T., Soleimani Sardoo, F. and Kouhestani, S. (2019) Flood frequency analysis for the Iranian interior deserts using the method of L?moments: A case study in the Loot River Basin. Natural Resource Modeling, v.32, pp.e12208.
Mohanta, N.R., Patel, N., Beck, K., Samantaray, S., and Sahoo, A. (2020a) Efficiency of River Flow Prediction in River Using Wavelet-CANFIS: A Case Study. In Intelligent Data Engineering and Analytics (pp. 435-443). Springer, Singapore.
Mohanta, N.R., Biswal, P., Kumari, S.S., Samantaray, S., and Sahoo, A. (2020b) Estimation of Sediment Load Using Adaptive Neuro-Fuzzy Inference System at Indus River Basin, India, In Intelligent Data Engineering and Analytics (pp. 427-434). Springer, Singapore.
Montanari, A. and Grossi, G. (2008) Estimating the uncertainty of hydrological forecasts: A statistical approach. Water Resour. Res., v.44(12).
Mujere, N. (2006) Impact of river flow changes on irrigation agriculture: A case study of Nyanyadzi irrigation scheme in Chimanimani district. MPhil Thesis, University of Zimbabwe, Harare.
Mujere, N. (2011) Flood frequency analysis using the Gumbel distribution. Internat. Jour. Computer Sci. Engg., v.3, pp.2774-2778.
Naz, S., Baig, M.J., Inayatullah, S., Siddiqi, T.A. and Ahsanuddin, M., (2019) Flood Risk Assessment of Guddu Barrage using Gumbel's Distribution. Internat. Jour. Sci., v.8(04), pp.33-38.
Odiyo, J., Mathivha, F.I., Nkuna, T.R. and Makungo, R. (2019) Hydrological hazards in Vhembe district in Limpopo Province, South Africa. Jàmbá: Jour. Disaster Risk Studies, v.11, pp.1-13.
Olumide, B.A., Saidu, M. and Oluwasesan, A. (2013) Evaluation of best fit probability distribution models for the prediction of rainfall and runoff volume (Case Study Tagwai Dam, Minna-Nigeria). Internat. Jour. Engg. Tech., v.3, pp.94-98.
Poul, A.K., Shourian, M. and Ebrahimi, H. (2019) A comparative study of MLR, KNN, ANN and ANFIS models with wavelet transform in monthly stream flow prediction. Water Resour. Managmt., v.33, pp.2907-2923.
Rezaeianzadeh, M., Tabari, H., Yazdi, A.A., Isik, S. and Kalin, L. (2014) Flood flow forecasting using ANN, ANFIS and regression models. Neural Computing and Applications, v.25, pp.25-37.
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.
Sahoo, A., Samantaray, S., Bankuru, S., and Ghose, D.K. (2020) Prediction of Flood Using Adaptive Neuro-Fuzzy Inference Systems: A Case Study, In: Smart Intelligent Computing and Applications (pp. 733-739). Springer, Singapore.
Sarma, P. (1999) Flood risk zone mapping of Dikrong sub basin in Assam. In: Proceedings of the Map India 1999 Conference: 24-26 August 1999; New Delhi, India.
Samantaray, S., and Ghose, D.K. (2019) Dynamic Modelling of Runoff in a Watershed Using Artificial Neural Network, In: Smart Intelligent Computing and Applications (pp.561-568). Springer, Singapore.
Samantaray, S., Sahoo, A., and Ghose, D.K. (2019) Assessment of Groundwater Potential Using Neural Network: A Case Study, In International Conference on Intelligent Computing and Communication (pp. 655-664). Springer, Singapore.
Samantaray, S., and Sahoo, A. (2020a) 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., and Sahoo, A. (2020b) 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.
Samantaray, S., and Sahoo, A. (2020c) 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. (2020d) Estimation of flood frequency using statistical method: Mahanadi river basin, India. h2oj, v.3(1), pp.189-207,
Samantaray, S., Sahoo, A., and Ghose, D.K. (2020) Prediction of Sedimentation in an Arid Watershed Using BPNN and ANFIS, In ICT Analysis and Applications, (pp. 295-302). Springer, Singapore.
Sehgal, V., Sahay, R.R. and Chatterjee, C. (2014) Effect of utilization of discrete wavelet components on flood forecasting performance of wavelet based ANFIS models. Water Resour. Managmt., v.28, pp.1733-1749.
Seo, Y., Kim, S. and Singh, V.P. (2015) Multistep-ahead flood forecasting using wavelet and data-driven methods. KSCE Jour. Civil Engg., v.19, pp.401-417.
Shaw, E.M. (1983) Hydrology in Practice. Van Nostrand Reinhold, UK Sihag, P., Esmaeilbeiki, F., Singh, B., Ebtehaj, I. and Bonakdari, H. (2019) Modeling unsaturated hydraulic conductivity by hybrid soft computing techniques. Soft Computing, v.23, pp.12897-12910.
Solomon, O. and Prince, O. (2013) Flood frequency analysis of Osse river using Gumbel's distribution. Civil Environ. Res., v.3, pp.55-59.
Ullah, N. and Choudhury, P. (2010) Flood Forecasting in River System Using ANFIS. In: AIP Conf. Proc., pp.694-699.
Wu, Y., Lall, U., Lima, C.H.R. and Zhong, P. (2018) Local and regional flood frequency analysis based on hierarchical Bayesian model: application to annual maximum streamflow for the Huaihe River basin. Hydrology and Earth System Sciences Discussions, pp.1-21.
Yang, X.-S. (2010) Firefly algorithm, stochastic test functions and design optimisation. arXiv:1003.1409v1 [math.OC], pp.78-84.
Yaseen, Z.M., Ebtehaj, I., Bonakdari, H., Deo, R.C., Mehr, A.D., Mohtar, W.H.M.W., Diop, L., El-Shafie, A. and Singh, V.P., (2017) Novel approach for streamflow forecasting using a hybrid ANFIS-FFA model. Jour. Hydrol., v.554, pp.263-276.
Zelenhasic, E.F. (1970) Theoretical probability distributions for flood peaks. Hydrology papers (Colorado State University); no. 42.
Zhang, G., Patuwo, B.E. and Hu, M.Y. (1998) Forecasting with artificial neural networks:: The state of the art. Internat. Jour. Forecasting, v.14, pp.35-62.
Zhou, J., Li, C., Arslan, C.A., Hasanipanah, M. and Amnieh, H.B. (2019a) Performance evaluation of hybrid FFA-ANFIS and GA-ANFIS models to predict particle size distribution of a muck-pile after blasting. Engineering with Computers, pp.1-10.
Zhou, Y., Guo, S. and Chang, F.-J. (2019b) Explore an evolutionary recurrent ANFIS for modelling multi-step-ahead flood forecasts. Jour. hydrol., v.570, pp.343-355.
