Abstract：
Drought as a natural phenomenon has always been a serious threat to regions with hot and dry climates. One of the major effects of drought is the drop in groundwater level. This paper focused on the SPI (Standardized Precipitation Index) and SWI (Standardized Water-Level Index) to assess meteorological and hydrological drought, respectively. In the first part, we used different time frames of SPI (3, 6, 12, and 24 months) to investigate drought in Yazd, a dry province in the center of Iran for 29 years (1990-2018). Then, in the second part, the relationship between SPI and SWI was investigated in the three aquifers of Yazd by some rain gauge stations and the closest observation wells to them. In addition to using SPI and SWI, we also used different machine learning (ML) algorithms to predict drought conditions including linear model and six non-linear models of K_Nearest_Neighbors, Gradient_Boosting, Decision_Tree, XGBoost, Random_Forest, and Neural_Net. To evaluate the accuracy of the mentioned models, three statistical indicators including Score, RMSE, and MAE were used. Based on the results of the first part, Yazd province has changed from mild wet to mild drought in terms of meteorological drought (the amount of rainfall according to SPI), and this condition can worsen due to climate change. The models used in ML showed that SPI-6 (score ave = 0.977), SPI-3 (score ave = 0.936), SPI-24 (score ave = 0.571), and SPI-12 (score ave = 0.413) indices had the highest accuracy, respectively. The models of Neural_Net (score ave = 0.964-RMSE ave = 0.020-MAE ave = 0.077) and Gradient_Boosting (score ave = 0.551-RMSE ave = 0.124-MAE ave = 0.248) had the highest and lowest accuracy in prediction of the SPI in all four-time scales. Based on the results of the second part, about the SWI, Random_Forest model (score = 0.929-RMSE = 0.052-MAE = 0.150) and model of Neural_Net (score = 0.755-RMSE = 0.235-MAE = 0.456) had the highest and lowest accuracy, respectively. Also, hydrological drought (reduction of the groundwater level) of the region has been much more severe, and according to the low correlation coefficient of average SPI and SWI (R2 = 0.14), we found that the uncontrolled pumping wells, as a main factor than a shortage of rainfall, have aggravated the hydrological drought, and this region is at risk of becoming a more arid region in the future.
摘要：
干旱作为一种自然现象，一直是炎热和干燥气候地区的严重威胁。干旱的主要影响之一是地下水位的下降。本文重点研究了SPI（标准化降水指数）和SWI（标准化水位指数）来评估气象和水文干旱，分别。在第一部分,我们使用不同的SPI时间框架(3、6、12和24个月)来调查亚兹德的干旱，伊朗中部的干旱省份已有29年（1990-2018年）。然后，在第二部分,通过一些雨量计站和最接近它们的观测井，在亚兹德的三个含水层中研究了SPI和SWI之间的关系。除了使用SPI和SWI，我们还使用不同的机器学习（ML）算法来预测干旱条件，包括K_Nearest_Neighbors的线性模型和六个非线性模型，梯度_提升，决策树，XGBoost,Random_Forest,和神经网络。为了评估上述模型的准确性，包括得分在内的三个统计指标，RMSE,使用MAE。根据第一部分的结果，亚兹德省在气象干旱(根据SPI的降雨量)方面由轻度湿润转为轻度干旱,由于气候变化，这种情况可能会恶化。ML中使用的模型显示SPI-6(得分为ave=0.977)，SPI-3（评分ave=0.936），SPI-24（评分ave=0.571），SPI-12(得分为ave=0.413)指数的准确性最高，分别。Neural_Net（评分ave=0.964-RMSEave=0.020-MAEave=0.077）和Gradient_Boosting（评分ave=0.551-RMSEave=0.124-MAEave=0.248）的模型在所有四个时间尺度上的SPI预测精度最高和最低。根据第二部分的结果，关于SWI,随机森林模型(得分=0.929-RMSE=0.052-MAE=0.150)和神经网络模型(得分=0.755-RMSE=0.235-MAE=0.456)的准确度最高和最低,分别。此外，该地区的水文干旱(地下水位下降)要严重得多，根据平均SPI和SWI的低相关系数(R2=0.14)，我们发现不受控制的抽水井，作为降雨不足的主要因素，加剧了水文干旱，该地区将来有可能成为更干旱的地区。
