A Comparison of Emotional Neural Network (ENN) and Artificial Neural Network (ANN) Approach for Rainfall-Runoff Modelling
Reliable method of rainfall-runoff modeling is a prerequisite for proper management and mitigation of extreme events such as floods. The objective of this paper is to contrasts the hydrological execution of Emotional Neural Network (ENN) and Artificial Neural Network (ANN) for modelling rainfall-runoff in the Sone Command, Bihar as this area experiences flood due to heavy rainfall. ENN is a modified version of ANN as it includes neural parameters which enhance the network learning process. Selection of inputs is a crucial task for rainfall-runoff model. This paper utilizes cross correlation analysis for the selection of potential predictors. Three sets of input data: Set 1, Set 2 and Set 3 have been prepared using weather and discharge data of 2 raingauge stations and 1 discharge station located in the command for the period 1986-2014. Principal Component Analysis (PCA) has then been performed on the selected data sets for selection of data sets showing principal tendencies. The data sets obtained after PCA have then been used in the model development of ENN and ANN models. Performance indices were performed for the developed model for three data sets. The results obtained from Set 2 showed that ENN with R= 0.933, R2 = 0.870, Nash Sutcliffe = 0.8689, RMSE = 276.1359 and Relative Peak Error = 0.00879 outperforms ANN in simulating the discharge. Therefore, ENN model is suggested as a better model for rainfall-runoff discharge in the Sone command, Bihar.
Chang, Tak Kwin, Amin Talei, Sina Alaghmand, and Melanie Po-Leen Ooi. “Choice of Rainfall Inputs for Event-Based Rainfall-Runoff Modeling in a Catchment with Multiple Rainfall Stations Using Data-Driven Techniques.” Journal of Hydrology 545 (February 2017): 100–108. doi:10.1016/j.jhydrol.2016.12.024.
Jain, Ashu, and Sanaga Srinivasulu. “Development of Effective and Efficient Rainfall-Runoff Models Using Integration of Deterministic, Real-Coded Genetic Algorithms and Artificial Neural Network Techniques.” Water Resources Research 40, no. 4 (April 2004). doi:10.1029/2003wr002355.
Osman, Yassin Z., and Mawada E. Abdellatif. “Improving Accuracy of Downscaling Rainfall by Combining Predictions of Different Statistical Downscale Models.” Water Science 30, no. 2 (October 2016): 61–75. doi:10.1016/j.wsj.2016.10.002.
Shoaib, Muhammad, Asaad Y. Shamseldin, Sher Khan, Mudasser Muneer Khan, Zahid Mahmood Khan, and Bruce W. Melville. “A Wavelet Based Approach for Combining the Outputs of Different Rainfall–runoff Models.” Stochastic Environmental Research and Risk Assessment 32, no. 1 (November 29, 2016): 155–168. doi:10.1007/s00477-016-1364-x.
Chang, Tak Kwin, Amin Talei, Chai Quek, and Valentijn R.N. Pauwels. “Rainfall-Runoff Modelling Using a Self-Reliant Fuzzy Inference Network with Flexible Structure.” Journal of Hydrology 564 (September 2018): 1179–1193. doi:10.1016/j.jhydrol.2018.07.074.
Nourani, Vahid. “An Emotional ANN (EANN) Approach to Modeling Rainfall-Runoff Process.” Journal of Hydrology 544 (January 2017): 267–277. doi:10.1016/j.jhydrol.2016.11.033.
Rezaie-balf, Mohammad, Sujay Raghavendra Naganna, Alireza Ghaemi, and Paresh Chandra Deka. “Wavelet Coupled MARS and M5 Model Tree Approaches for Groundwater Level Forecasting.” Journal of Hydrology 553 (October 2017): 356–373. doi:10.1016/j.jhydrol.2017.08.006.
Bartoletti, N., F. Casagli, S. Marsili-Libelli, A. Nardi, and L. Palandri. “Data-Driven Rainfall/runoff Modelling Based on a Neuro-Fuzzy Inference System.” Environmental Modelling & Software 106 (August 2018): 35–47. doi:10.1016/j.envsoft.2017.11.026.
Chandwani, Vinay, Sunil Kumar Vyas, Vinay Agrawal, and Gunwant Sharma. “Soft Computing Approach for Rainfall-Runoff Modelling: A Review.” Aquatic Procedia 4 (2015): 1054–1061. doi:10.1016/j.aqpro.2015.02.133.
Sharma, S.K., and K.N. Tiwari. “Bootstrap Based Artificial Neural Network (BANN) Analysis for Hierarchical Prediction of Monthly Runoff in Upper Damodar Valley Catchment.” Journal of Hydrology 374, no. 3–4 (August 2009): 209–222. doi:10.1016/j.jhydrol.2009.06.003.
Ghumman, A.R., Yousry M. Ghazaw, A.R. Sohail, and K. Watanabe. “Runoff Forecasting by Artificial Neural Network and Conventional Model.” Alexandria Engineering Journal 50, no. 4 (December 2011): 345–350. doi:10.1016/j.aej.2012.01.005.
Hlavčová, Kamila, Zuzana Štefunková, Peter Valent, Silvia Kohnová, Roman Výleta, and Ján Szolgay. “Modelling the Climate Change Impact On Monthly Runoff in Central Slovakia.” Procedia Engineering 161 (2016): 2127–2132. doi:10.1016/j.proeng.2016.08.804.
Kabiri, R., V. Kanani, and C. Andrew. "Climate Change Impacts on River Runoff in Klang Watershed in West Malaysia." J. Clim. Res 48 (2012): 57-71.
Lee, Kwan Tun, Jui-Yi Ho, Hong-Ming Kao, Gwo-Fong Lin, and Tsun-Hua Yang. “Using Ensemble Precipitation Forecasts and a Rainfall-Runoff Model for Hourly Reservoir Inflow Forecasting During Typhoon Periods.” Journal of Hydro-Environment Research 22 (January 2019): 29–37. doi:10.1016/j.jher.2018.05.002.
Wu, C.L., and K.W. Chau. “Rainfall–runoff Modeling Using Artificial Neural Network Coupled with Singular Spectrum Analysis.” Journal of Hydrology 399, no. 3–4 (March 2011): 394–409. doi:10.1016/j.jhydrol.2011.01.017.
Yaduvanshi, Aradhana, Rajat K. Sharma, Sarat C. Kar, and Anand K. Sinha. “Rainfall–runoff Simulations of Extreme Monsoon Rainfall Events in a Tropical River Basin of India.” Natural Hazards 90, no. 2 (October 31, 2017): 843–861. doi:10.1007/s11069-017-3075-0.
Demirel, Mehmet C., Anabela Venancio, and Ercan Kahya. “Flow Forecast by SWAT Model and ANN in Pracana Basin, Portugal.” Advances in Engineering Software 40, no. 7 (July 2009): 467–473. doi:10.1016/j.advengsoft.2008.08.002.
Maier, Holger R., and Graeme C. Dandy. “Neural Networks for the Prediction and Forecasting of Water Resources Variables: a Review of Modelling Issues and Applications.” Environmental Modelling & Software 15, no. 1 (January 2000): 101–124. doi:10.1016/s1364-8152(99)00007-9.
Tiwari, Mukesh K., and Chandranath Chatterjee. “A New wavelet–bootstrap–ANN Hybrid Model for Daily Discharge Forecasting.” Journal of Hydroinformatics 13, no. 3 (July 2011): 500–519. doi:10.2166/hydro.2010.142.
Adamowski, Jan, and Karen Sun. “Development of a Coupled Wavelet Transform and Neural Network Method for Flow Forecasting of Non-Perennial Rivers in Semi-Arid Watersheds.” Journal of Hydrology 390, no. 1–2 (August 2010): 85–91. doi:10.1016/j.jhydrol.2010.06.033.
Solomatine, Dimitri P., and Khada N. Dulal. “Model Trees as an Alternative to Neural Networks in Rainfall—runoff Modelling.” Hydrological Sciences Journal 48, no. 3 (June 2003): 399–411. doi:10.1623/hysj.48.3.399.45291.
Wang, Wensheng, and Jing Ding. "Wavelet network model and its application to the prediction of hydrology." Nature and Science 1, no. 1 (2003): 67-71.
Nourani, Vahid, Mehdi Komasi, and Akira Mano. “A Multivariate ANN-Wavelet Approach for Rainfall–Runoff Modeling.” Water Resources Management 23, no. 14 (February 3, 2009): 2877–2894. doi:10.1007/s11269-009-9414-5.
Roshni, Thendiyath, Madan K. Jha, Ravinesh C. Deo, and A. Vandana. “Development and Evaluation of Hybrid Artificial Neural Network Architectures for Modeling Spatio-Temporal Groundwater Fluctuations in a Complex Aquifer System.” Water Resources Management 33, no. 7 (April 18, 2019): 2381–2397. doi:10.1007/s11269-019-02253-4.
Seo, Youngmin, Sungwon Kim, Ozgur Kisi, and Vijay P. Singh. “Daily Water Level Forecasting Using Wavelet Decomposition and Artificial Intelligence Techniques.” Journal of Hydrology 520 (January 2015): 224–243. doi:10.1016/j.jhydrol.2014.11.050.
Du, Kongchang, Ying Zhao, and Jiaqiang Lei. “The Incorrect Usage of Singular Spectral Analysis and Discrete Wavelet Transform in Hybrid Models to Predict Hydrological Time Series.” Journal of Hydrology 552 (September 2017): 44–51. doi:10.1016/j.jhydrol.2017.06.019.
Mallat, S.G. “A Theory for Multiresolution Signal Decomposition: The Wavelet Representation.” IEEE Transactions on Pattern Analysis and Machine Intelligence 11, no. 7 (July 1989): 674–693. doi:10.1109/34.192463.
Rezaie-Balf, Mohammad, Zahra Zahmatkesh, and Sungwon Kim. “Soft Computing Techniques for Rainfall-Runoff Simulation: Local Non–Parametric Paradigm Vs. Model Classification Methods.” Water Resources Management 31, no. 12 (June 1, 2017): 3843–3865. doi:10.1007/s11269-017-1711-9.
Sharghi, Elnaz, Vahid Nourani, Hessam Najafi, and Amir Molajou. “Emotional ANN (EANN) and Wavelet-ANN (WANN) Approaches for Markovian and Seasonal Based Modeling of Rainfall-Runoff Process.” Water Resources Management 32, no. 10 (May 8, 2018): 3441–3456. doi:10.1007/s11269-018-2000-y.
Fellous, Jean-Marc. “Neuromodulatory Basis of Emotion.” The Neuroscientist 5, no. 5 (September 1999): 283–294. doi:10.1177/107385849900500514.
Nourani, Vahid, Özgür Kisi, and Mehdi Komasi. “Two Hybrid Artificial Intelligence Approaches for Modeling Rainfall–runoff Process.” Journal of Hydrology 402, no. 1–2 (May 2011): 41–59. doi:10.1016/j.jhydrol.2011.03.002.
Sharghi, Elnaz, Vahid Nourani, Amir Molajou, and Hessam Najafi. “Conjunction of Emotional ANN (EANN) and Wavelet Transform for Rainfall-Runoff Modeling.” Journal of Hydroinformatics 21, no. 1 (October 9, 2018): 136–152. doi:10.2166/hydro.2018.054.
Latinez Sotomayor, Karen A. "Comparison of adaptive methods using multivariate regression splines (MARS) and artificial neural networks backpropagation (ANNB) for the forecast of rain and temperatures in the Mantaro river basin." (March 2010).
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