Performance evaluation of ANN-WOA and ANN-BWO simulation-optimization methods in predicting daily runoff (case study: Jelogir station in Karkheh watershed)

Document Type : Research Paper

Authors

1 Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.

2 Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

3 Department of Range and Watershed Management, Urmia University, Urmia, Iran.

4 Department of Water Engineering, Center of Excellence, Faculty of Civil Engineering, University of Tabriz -Farazab Consulting Engineers, PMO, Tabriz, Iran .

Abstract

The key to social and economic development is water, an essential natural resource. Worldwide, many areas are experiencing water supply and demand mismatches or are under extreme stress due to water shortages. Water resources have been mismanaged or limited due to an increase in demand and limitations in available water supply (Banadkooki et al., 2019). Rainfall and runoff are considered to be the main components of the hydrological cycle. In order to capture the dynamic relationship between rainfall and runoff, engineers need to develop an accurate model (Tikhamarine et al., 2022). Rainfall-runoff modeling is one of the methods of estimating runoff and a suitable tool for studying hydrological processes, evaluating water resources and watershed management (Abrahart and See, 2000). But the complexity and non-linear nature of the rainfall-runoff process and the unknown effect of the factors on each other and finally on the outflow of the basin make modeling more difficult (Moriasi et al., 2007). Therefore, it is necessary to use methods that, in addition to dynamism, have the ability to develop, have a conceptual and user-friendly structure (Shi et al., 2012). The role and importance of the mentioned process in water resources studies has caused this process to be noticed by experts (Guven, 2009). Therefore, several methods such as artificial neural networks, fuzzy and neuro-fuzzy systems, wavelet analysis, genetic algorithm, genetic programming and stochastic differential equations have been developed to model the rainfall-runoff process (Yaseen et al., 2016; Zhang et al., 2019). The development of rainfall-runoff models using different AI models has been conducted several times in the past two decades, but these models still have several shortcomings. These drawbacks are usually related to overfitting, difficulty in initializing the internal parameters related to these models and proposing the proper input-output architecture of the model (Ahmed et al., 2019).

Keywords

Main Subjects

References

  • Abrahart, R.J. and See, L., 2000. Comparing neural network and autoregressive moving average techniques for the provision of continuous river flow forecasts in two contrasting catchments. Hydrological processes, 14(11‐12), pp.2157-2172. Doi: 10.1002/1099-1085(20000815/30)14:11/12<2157::AID-HYP57>3.0.CO;2-S.

 

  • Abudu, S., Cui, C.L., King, J.P. and Abudukadeer, K., 2010. Comparison of performance of statistical models in forecasting monthly streamflow of Kizil River, China. Water Science and Engineering, 3(3), pp.269-281.Doi: 10.3882/j.issn.1674-2370.2010.03.003.

 

  • Afan, H.A., El-Shafie, A., Yaseen, Z.M., Hameed, M.M., Wan Mohtar, W.H.M. and Hussain, A., 2015. ANN based sediment prediction model utilizing different input scenarios. Water resources management, 29, pp.1231-1245.

 

  • Ahmed, J.A. and Sarma, A.K., 2007. Artificial neural network model for synthetic streamflow generation. Water resources management, 21, pp.1015-1029.

 

  • Anusree, K. and Varghese, K.O., 2016. Streamflow prediction of Karuvannur River Basin using ANFIS, ANN and MNLR models. Procedia Technology, 24, pp.101-108. Doi: 1016/j.protcy.2016.05.015.

 

  • Ayele, G.T., Teshale, E.Z., Yu, B., Rutherfurd, I.D. and Jeong, J., 2017. Streamflow and sediment yield prediction for watershed prioritization in the Upper Blue Nile River Basin, Ethiopia. Water, 9(10), p.782. Doi: 3390/w9100782.

 

  • Box, G.P., & Jenkins, 1970. Time series analysis: forecasting and control, San Francisco: John Wiley & Sons.

 

  • Chiang, Y.M., Chang, L.C. and Chang, F.J., 2004. Comparison of static-feedforward and dynamic-feedback neural networks for rainfall–runoff modeling. Journal of hydrology, 290(3-4), pp.297-311. Doi: 1016/j.jhydrol.2003.12.033.

 

  • Danandeh Mehr, A., Kahya, E., Şahin, A. and Nazemosadat, M.J., 2015. Successive-station monthly streamflow prediction using different artificial neural network algorithms. International Journal of Environmental Science and Technology, 12, pp.2191-2200.

 

  • Daneshvar Vousoughi, F. and Samadzadeh, R., 2021. Predicting runoff with pre-processing approaches in Ardabil plain. Hydrogeomorphology, 8(26), pp.116-99. Doi: 22034/hyd.2021.44060.1570. (In Persian).

 

  • Fan, Q., Chen, Z., Zhang, W. and Fang, X., 2022. ESSAWOA: enhanced whale optimization algorithm integrated with salp swarm algorithm for global optimization. Engineering with Computers, 38(Suppl 1), pp.797-814.

 

  • Freire, P.K.D.M.M., Santos, C.A.G. and da Silva, G.B.L., 2019. Analysis of the use of discrete wavelet transforms coupled with ANN for short-term streamflow forecasting. Applied Soft Computing, 80, pp.494-505.Doi: 1016/j.asoc.2019.04.024.

 

  • Wiley, D., Ware, C., Bocconcelli, A., Cholewiak, D., Friedlaender, A., Thompson, M. and Weinrich, M., 2011. Underwater components of humpback whale bubble-net feeding behaviour. Behaviour, pp.575-602.

 

  • Ghorbani, M.A. and Dehghani, R., 2016. Application of Bayesian Neural Networks, Support Vector Machines and Gene Expression Programming Analysis of Rainfall-Runoff Monthly (Case Study: Kakarza River). Irrigation Science and Engineering, 39(2), pp.125-138.

 

  • Ghorbani, M.A.,Azani, A. and Mahmoudi Vanolya, S. 2016 . Rainfall-Runoff Modeling Using Hybrid Intelligent Models, Iran Water Resources Research, 11(2), 146 (In Persian).

 

  • Guven, A., 2009. Linear genetic programming for time-series modelling of daily flow rate. Journal of earth system science, 118, pp.137-146.

 

  • Haykin S. 1999. Neural Networks. MacMillan Publishing Company.

 

  • Houssein, E.H., Helmy, B.E.D., Oliva, D., Elngar, A.A. and Shaban, H., 2021. A novel black widow optimization algorithm for multilevel thresholding image segmentation. Expert Systems with Applications, 167, p.114159. Doi: 1016/j.eswa.2020.114159.

 

  • Kember, G., Flower, A.C. and Holubeshen, J., 1993. Forecasting river flow using nonlinear dynamics. Stochastic Hydrology and Hydraulics, 7, pp.205-212.

 

  • Legates, D.R. and McCabe Jr, G.J., 1999. Evaluating the use of “goodness‐of‐fit” measures in hydrologic and hydroclimatic model validation. Water resources research, 35(1), pp.233-241. Doi: 1029/1998WR900018.

 

  • - Maier, H.R., Kapelan, Z., Kasprzyk, J., Kollat, J., Matott, L.S., Cunha, M.C., Dandy, G.C., Gibbs, M.S., Keedwell, E., Marchi, A. and Ostfeld, A., 2014. Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions. Environmental Modelling & Software, 62, pp.271-299. Doi: 1016/j.envsoft.2014.09.013.

 

  • Mirjalili, S. and Lewis, A., 2016. The whale optimization algorithm. Advances in engineering software, 95, pp.51-67.Doi: 1016/j.advengsoft.2016.01.008.

 

  • Mohanty, U.C., Nageswararao, M.M., Sinha, P., Nair, A., Singh, A., Rai, R.K., Kar, S.C., Ramesh, K.J., Singh, K.K., Ghosh, K. and Rathore, L.S., 2019. Evaluation of performance of seasonal precipitation prediction at regional scale over India. Theoretical and Applied Climatology, 135, pp.1123-1142.

 

  • Moriasi, D.N., Arnold, J.G., Van Liew, M.W., Bingner, R.L., Harmel, R.D. and Veith, T.L., 2007. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the ASABE, 50(3), pp.885-900.

 

  • Nayak, P.C., Rao, Y.S. and Sudheer, K.P., 2006. Groundwater level forecasting in a shallow aquifer using artificial neural network approach. Water resources management, 20, pp.77-90.Water resources management, 20, 77-90.

 

  • Rana, N., Latiff, M.S.A., Abdulhamid, S.I.M. and Chiroma, H., 2020. Whale optimization algorithm: a systematic review of contemporary applications, modifications and developments. Neural Computing and Applications, 32, pp.16245-16277.

 

  • Salajegheh, A., Fathabadi,A and Mahdavi, M.  2009. Investigation on the efficiency of neuro-fuzzy method and statistical models in simulation of rainfall-runoff process, Journal of Range and Watershed Management, 62(1), 65 (In Persian).
  • Salas, J.D., 1980. Applied modeling of hydrologic time series. Water Resources Publication.

 

  • Sari, I., Zengin, S., Davutoglu, V., Yildirim, C. and Gunay, N., 2008. Myocarditis after black widow spider envenomation. The American journal of emergency medicine, 26(5), pp.630-e1.Doi: 1016/j.ajem.2007.09.012.

 

  • Shi, J., Guo, J. and Zheng, S., 2012. Evaluation of hybrid forecasting approaches for wind speed and power generation time series. Renewable and Sustainable Energy Reviews, 16(5), pp.3471-3480. Doi: 1016/j.rser.2012.02.044.

 

  • Valipour, M. and Montazar, A.A., 2012. Sensitive analysis of optimized infiltration parameters in SWDC model. Advances in Environmental Biology, pp.2574-2582.

 

  • Watkins, W.A. and Schevill, W.E., 1979. Aerial observation of feeding behavior in four baleen whales: Eubalaena glacialis, Balaenoptera borealis, Megaptera novaeangliae, and Balaenoptera physalus. Journal of Mammalogy, 60(1), pp.155-163. Doi: 2307/1379766.

 

  • Yaseen, Z.M., Awadh, S.M., Sharafati, A. and Shahid, S., 2018. Complementary data-intelligence model for river flow simulation. Journal of Hydrology, 567, pp.180-190. Doi: 1016/j.jhydrol.2018.10.020.

 

  • Yaseen, Z.M., El-Shafie, A., Afan, H.A., Hameed, M., Mohtar, W.H.M.W. and Hussain, A., 2016. RBFNN versus FFNN for daily river flow forecasting at Johor River, Malaysia. Neural Computing and Applications, 27, pp.1533-1542.

 

  • Zhang, D., Peng, X., Pan, K. and Liu, Y., 2019. A novel wind speed forecasting based on hybrid decomposition and online sequential outlier robust extreme learning machine. Energy conversion and management, 180, pp.338-357. Doi: 1016/j.enconman.2018.10.089.
Irrigation Sciences and Engineering
Volume 47, Issue 3
October 2024
Pages 19-36

Files

History

  • Receive Date: 11 October 2022
  • Revise Date: 16 September 2023
  • Accept Date: 17 September 2023
  • Publish Date: 22 October 2024

Share

How to cite

Statistics