Water and Irrigation Management

Water and Irrigation Management

Development of artificial neural network and particle swarm algorithm to predict inflow to dams under the influence of climate scenarios

Document Type : Research Paper

Authors
Mehrnoosh Hedayatizadeh
Saeed Jamali
hooman hajikandi
Somayeh Yousefi
Department of Civil Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran.
10.22059/jwim.2024.376149.1160
Abstract
Climate change causes changes in the flow of rivers by causing changes in temperature and precipitation. Therefore, river flow simulation is important as a prerequisite for some environmental and engineering issues. In the current research, the effect of climate change on the Mahabad’s river flow in the future periods (2045-2026) was predicted using machine learning models. First, two input scenarios were compiled, in which the first scenario included temperature and precipitation parameters and the second scenario included temperature, precipitation, and flow parameters one month ago. In the following, the performance of two ANN and ANN-PSO models in estimating the flow rate in the base period (1992-2014) was compared to select the best scenario and the best model for predicting the flow in the future period under the three scenarios SSP1.26, SSP2.45 and SSP5.85 of the CMIP6. The results of the error evaluation criteria showed that the ANN-PSO model makes the best estimation of the river flow using the second scenario and with the criteria (NSE=0.77, RMSE=6.4 MCM, MAE=3.4 MCM for the test data) and it was chosen to predict the flow in the future period (2026-2045). The results of investigating the effect of climate change on each of the meteorological parameters showed that climate change causes an increase in temperature and creates a fluctuating pattern in precipitation. The results of the climate change survey on flow showed that under the SSP1.26 scenario, there will not be much changes in flow in almost months, but in the SSP2.45 and SSP585 scenarios, there will be an increase in the discharge in December, and in May and April, the greatest decrease in discharge will be (16.50 MCM) and (13.33 MCM) respectively.
Keywords
Artificial neural network
Climate change
particle swarm algorithm
River discharge
Time series prediction
Subjects
  1. Arya Azar, N., Ghordoyee Milan, S., & Kayhomayoon, Z. (2021). Predicting monthly evaporation from dam reservoirs using LS-SVR and ANFIS optimized by Harris hawks optimization algorithm. Environmental Monitoring and Assessment, 193(11), 1-14.
  2. Ashrafzadeh, A., Malik, A., Jothiprakash, V., Ghorbani, M. A., & Biazar, S. M. (2020). Estimation of daily pan evaporation using neural networks and meta-heuristic approaches. ISH Journal of Hydraulic Engineering, 26(4), 421-429.
  3. Bac, B. H., Nguyen, H., Thao, N. T. T., Hanh, V. T., Duyen, L. T., Dung, N. T., .. & Hiep, N. H. (2021). Estimating heavy metals absorption efficiency in an aqueous solution using nanotube-type halloysite from weathered pegmatites and a novel Harris hawks optimization-based multiple layers perceptron neural network. Engineering with Computers, 1-16.
  4. Chang, H., & Jung, I. W. (2010). Spatial and temporal changes in runoff caused by climate change in a complex large river basin in Oregon. Journal of Hydrology, 388(3-4), 186-207.
  5. Daba, M. H. (2010). Sensitivity of SWAT Simulated Runoff to Temperature and Rainfall in the Upper Awash Sab-Basin, Ethiopia. Hydrol Current Res 9: 293. doi: 10.4172/2157-7587.1000293 Page 2 of 7 Volume 9• Issue 1• 1000293 Hydrol Current Res, an open access journal ISSN: 2157-7587 The daily weather data required to run the SWAT hydrological model were acquired from the National Meteorology Agency (NMA). The daily data for maximum and minimum temperature, rainfall, relative humidity, and wind speed were obtained (Doctoral dissertation, These data cover a period of 30 years from 1980 to).
  6. Daba, M. H., & You, S. (2020). Assessment of climate change impacts on river flow regimes in the upstream of Awash Basin, Ethiopia: based on IPCC fifth assessment report (AR5) climate change scenarios. Hydrology, 7(4), 98.
  7. Daba, M., & Rao, G. N. (2016). Evaluating potential impacts of climate change on hydro-meteorological variables in Upper Blue Nile Basin, Ethiopia: a case study of Finchaa Sub-basin. Journal of Environment and Earth Science, 6(5), 48-57.
  8. Di, C., Yang, X., & Wang, X. (2014). A four-stage hybrid model for hydrological time series forecasting. PloS one, 9(8), e104663.
  9. Emami, F., & Koch, M. (2019). Modeling the impact of climate change on water availability in the Zarrine River Basin and inflow to the Boukan Dam, Iran. Climate, 7(4), 51.
  10. Fathian, F., Mehdizadeh, S., Sales, A. K., & Safari, M. J. S. (2019). Hybrid models to improve the monthly river flow prediction: Integrating artificial intelligence and non-linear time series models. Journal of Hydrology, 575, 1200-1213.
  11. Fernando, A., Shamseldin, A., & Abrahart, R. (2011). Comparison of two data-driven approaches for daily river flow forecasting.
  12. Fiseha, B. M., Setegn, S. G., Melesse, A. M., Volpi, E., & Fiori, A. (2014). Impact of climate change on the hydrology of upper Tiber River Basin using bias corrected regional climate model. Water Resources Management, 28, 1327-1343.
  13. Ghasemain, B., Asl, D. T., Pham, B. T., Avand, M., Nguyen, H. D., & Janizadeh, S. J. V. J. O. E. S. (2020). Shallow landslide susceptibility mapping: A comparison between classification and regression tree and reduced error pruning tree algorithms. Vietnam Journal of Earth Sciences, 42(3), 208-227.
  14. Ghorbani, M. A., Deo, R. C., Yaseen, Z. M., H. Kashani, M., & Mohammadi, B. (2018). Pan evaporation prediction using a hybrid multilayer perceptron-firefly algorithm (MLP-FFA) model: case study in North Iran. Theoretical and applied climatology, 133, 1119-1131.
  15. Goodarzi, E., Dastorani, M., & Talebi, A. (2015). Evaluation of the Change-factor and LARS-WG methods of downscaling for simulation of climatic variables in the future (Case study: Herat Azam Watershed, Yazd-Iran). Ecopersia, 3(1), 833-846.
  16. Halik, G., Anwar, N., Santosa, B., & Edijatno. (2015). Reservoir inflow prediction under GCM scenario downscaled by wavelet transform and support vector machine hybrid models. Advances in Civil Engineering, 2015(1), 515376.
  17. He, Z., Wen, X., Liu, H., & Du, J. (2014). A comparative study of artificial neural network, adaptive neuro fuzzy inference system and support vector machine for forecasting river flow in the semiarid mountain region. Journal of Hydrology, 509, 379-386.
  18. Hodgkins, G. A., Whitfield, P. H., Burn, D. H., Hannaford, J., Renard, B., Stahl, K., ... & Wilson, D. (2017). Climate-driven variability in the occurrence of major floods across North America and Europe. Journal of Hydrology, 552, 704-717.
  19. IPCC. Climate Change. (2013). In The Physical Science Basis Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change Edited by 2013; IPCC: Geneva, Switzerland.
  20. IPCC. Climate Change. (2017). The Physical Science Basis; IPCC: Geneva, Switzerland.
  21. Kayhomayoon, Z., Naghizadeh, F., Malekpoor, M., Arya Azar, N., Ball, J., & Ghordoyee Milan, S. (2023). Prediction of evaporation from dam reservoirs under climate change using soft computing techniques. Environmental Science and Pollution Research, 30(10), 27912-27935.
  22. Kusangaya, S., Warburton, M. L., Van Garderen, E. A., & Jewitt, G. P. (2014). Impacts of climate change on water resources in southern Africa: A review. Physics and Chemistry of the Earth, Parts a/b/c, 67, 47-54.
  23. Lupo, A., Kininmonth, W., Armstrong, J. S., & Green, K. (2013). Global climate models and their limitations. Climate change reconsidered II: Physical science, 9, 148.
  24. Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S. L., Péan, C., Berger, S., ... & Zhou, B. (2021). Climate change 2021: the physical science basis. Contribution of working group I to the sixth assessment report of the intergovernmental panel on climate change, 2(1), 2391.
  25. Meshram, S. G., Ghorbani, M. A., Shamshirband, S., Karimi, V., & Meshram, C. (2019). River flow prediction using hybrid PSOGSA algorithm based on feed-forward neural network. Soft Computing, 23, 10429-10438.
  26. Milan, S. G., Kayhomayoon, Z., Azar, N. A., Berndtsson, R., Ramezani, M. R., & Moghaddam, H. K. (2023). Using machine learning to determine acceptable levels of groundwater consumption in Iran. Sustainable Production and Consumption, 35, 388-400.
  27. Mitsova, D. (2014). Coupling land use change modeling with climate projections to estimate seasonal variability in runoff from an urbanizing catchment near Cincinnati, Ohio. ISPRS International Journal of Geo-Information, 3(4), 1256-1277.
  28. Nguyen, X. H. (2020). Combining statistical machine learning models with ARIMA for water level forecasting: The case of the Red river. Advances in Water Resources, 142, 103656.
  29. Sammen, S. S., Ghorbani, M. A., Malik, A., Tikhamarine, Y., AmirRahmani, M., Al-Ansari, N., & Chau, K. W. (2020). Enhanced artificial neural network with Harris hawks optimization for predicting scour depth downstream of ski-jump spillway. Applied Sciences, 10(15), 5160.
  30. Shariati, M., Mafipour, M. S., Mehrabi, P., Bahadori, A., Zandi, Y., Salih, M. N., ... & Poi-Ngian, S. (2019). Application of a hybrid artificial neural network-particle swarm optimization (ANN-PSO) model in behavior prediction of channel shear connectors embedded in normal and high-strength concrete. Applied sciences, 9(24), 5534.
  31. Taye, M. T., Dyer, E., Hirpa, F. A., & Charles, K. (2018). Climate change impact on water resources in the Awash basin, Ethiopia. Water, 10(11), 1560.
  32. Tikhamarine, Y., Malik, A., Kumar, A., Souag-Gamane, D., & Kisi, O. (2019). Estimation of monthly reference evapotranspiration using novel hybrid machine learning approaches. Hydrological sciences journal, 64(15), 1824-1842.
  33. Zabardast Rostami, H. A., Raeini Sarjaz, M., & Gholami Sefidkouhi, M. A. (2021). Assessment of Climate Change Effects on River Flow of Gelevard Dam Basin. Journal of Watershed Management Research, 12(24), 205-216.
  34. Zhang, H., Singh, V. P., Wang, B., & Yu, Y. (2016). CEREF: A hybrid data-driven model for forecasting annual streamflow from a socio-hydrological system. Journal of hydrology, 540, 246-256.
Water and Irrigation Management
Volume 14, Issue 4
Winter 2025
Pages 845-862