Kayhomayoon Zahra, Naghizadeh Fariba, Malekpoor Mohammadreza, Arya Azar Naser, Ball James, Ghordoyee Milan Sami
Department of Geology, Payame Noor University, Tehran, Iran.
Department of Water Engineering, College of Agricultural Sciences, University of Guilan, Rasht, Iran.
Environ Sci Pollut Res Int. 2023 Feb;30(10):27912-27935. doi: 10.1007/s11356-022-23899-5. Epub 2022 Nov 17.
This study aimed to predict evaporation from dam reservoirs using artificial intelligence considering climate change. Mahabad Dam, near Lake Urmia, in northwestern Iran, is used to investigate the proposed approach. There are three parts to the study presented herein. In the first part, two machine learning models, namely group method of data handling (GMDH) and least squares support vector regression (LS-SVR), were used to model the inflow to the dam reservoir. Temperature, precipitation, and inflow during the previous month from 1990 to 2017 were used as input data. In the second part, the evaporation from the dam reservoir was modeled using the adaptive neuro-fuzzy inference system (ANFIS) and optimized ANFIS using Harris hawks optimization (HHO) and the arithmetic optimization algorithm (AOA) optimization algorithms. The input parameters in this part were temperature, precipitation, inflow to the dam reservoir, along with evaporation from the dam reservoir in the previous month. In the third part, precipitation and temperature were predicted using the fifth report of IPCC based on RCP2.6, RCP4.5, and RCP8.5 scenarios for the period 2020-2040. Out of 28 models presented in the fifth report, EC-ERATH and FIO-ESM had the greatest similarity with observational data of temperature and precipitation, respectively. The results of scatter plots and Taylor's diagram showed the higher performance of LS-SVR (root mean square error (RMSE), mean absolute percentage error (MAPE), and Nash-Sutcliffe efficiency (NSE) of 8.65, 4.69, and 0.96) compared to GMDH (RMSE, MAPE, and NSE of 11.65, 7.81, and 0.93) in modeling the inflow. Moreover, both hybrid modes (AOA-ANFIS and HHO-ANFIS) improved the performance of ANFIS in modeling the evaporation from the dam reservoir. The RMSE, MAPE, and NSE values for ANFIS were 0.56, 0.52, and 0.89, respectively, while these values for the AOA-ANFIS (RMSE, MAPE, and NSE of 0.31, 0.24, and 0.93) and HHO-ANFIS (RMSE, MAPE, and NSE of 0.20, 0.30, and 0.96) were improved remarkably. The impact of climate change reduced the inflow to the dam reservoir by about 0.45, 0.80, and 1.7 MCM in RCP2.6, RCP4.5, and RCP8.5 scenarios, respectively. Also, the effect of climate change caused the evaporation from the dam reservoir to increase by about 0.2, 0.9, and 1 MCM in RCP2.6, RCP4.5, and RCP8.5 scenarios, respectively. The findings of this study show that the correct management of dam reservoirs needs to consider the potential effects of climate change in the future. Moreover, the hybrid machine learning models used in this study can be used to predict the amount of evaporation in other reservoirs.
本研究旨在利用人工智能预测考虑气候变化因素的大坝水库蒸发量。位于伊朗西北部乌尔米耶湖附近的马哈巴德大坝被用于研究该方法。本文的研究分为三个部分。第一部分，使用两种机器学习模型，即数据处理分组方法（GMDH）和最小二乘支持向量回归（LS - SVR），对大坝水库的入库流量进行建模。将1990年至2017年上月的温度、降水量和入库流量用作输入数据。第二部分，使用自适应神经模糊推理系统（ANFIS）对大坝水库的蒸发量进行建模，并使用哈里斯鹰优化算法（HHO）和算术优化算法（AOA）对ANFIS进行优化。此部分的输入参数包括温度、降水量、大坝水库的入库流量以及上月大坝水库的蒸发量。第三部分，基于RCP2.6、RCP4.5和RCP8.5情景，利用政府间气候变化专门委员会（IPCC）的第五次报告预测2020 - 2040年期间的降水量和温度。在第五次报告中提出的28个模型中，EC - ERATH和FIO - ESM分别与温度和降水量的观测数据具有最大相似性。散点图和泰勒图的结果表明，在对入库流量进行建模时，与GMDH（均方根误差（RMSE）、平均绝对百分比误差（MAPE）和纳什 - 萨特克利夫效率（NSE）分别为11.65、7.81和0.93）相比，LS - SVR（RMSE、MAPE和NSE分别为8.65、4.69和0.96）具有更高的性能。此外，两种混合模式（AOA - ANFIS和HHO - ANFIS）在对大坝水库蒸发量进行建模时均提高了ANFIS的性能。ANFIS的RMSE、MAPE和NSE值分别为0.56、0.52和0.89，而AOA - ANFIS（RMSE、MAPE和NSE分别为0.31、0.24和0.93）和HHO - ANFIS（RMSE、MAPE和NSE分别为0.20、0.30和0.96）的值有显著改善。在RCP2.6、RCP4.5和RCP8.5情景下，气候变化的影响分别使大坝水库的入库流量减少约0.45、0.80和1.7百万立方米。同样，气候变化的影响分别使RCP2.6、RCP4.5和RCP8.5情景下大坝水库的蒸发量增加约0.2、0.9和1百万立方米。本研究结果表明，大坝水库的正确管理需要考虑未来气候变化的潜在影响。此外，本研究中使用的混合机器学习模型可用于预测其他水库的蒸发量。
