Univrsity Of Tehran Press Water and Irrigation Management 2251-6298 14 4 2025 02 19 Development of artificial neural network and particle swarm algorithm to predict inflow to dams under the influence of climate scenarios Development of artificial neural network and particle swarm algorithm to predict inflow to dams under the influence of climate scenarios 845 862 98010 10.22059/jwim.2024.376149.1160 FA Mehrnoosh Hedayatizadeh Department of Civil Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran. Saeed Jamali Department of Civil Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran. Hooman Hajikandi Department of Civil Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran. Somayeh Yousefi Department of Civil Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran. Journal Article 2024 05 05 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. 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. https://jwim.ut.ac.ir/article_98010_832b2022626b8e0b760d29041328062b.pdf