Azimi Saeed, Azhdary Moghaddam Mehdi, Hashemi Monfared Seyed Arman
Civil Engineering Department, Faculty of Engineering, University of Sistan and Baluchestan, P.O. Box 9816745563-161, Zahedan, Iran.
J Environ Health Sci Eng. 2018 May 19;16(2):129-145. doi: 10.1007/s40201-018-0301-y. eCollection 2018 Dec.
The level of groundwater resources is changing rapidly and this requires the discovery of newer groundwater resources. Drought is one of the most significant natural phenomena affecting different aspects of human life and environment. During the last decades, the application of artificial intelligent techniques has been recognized as effective approaches to forecast an annual precipitation rate.
In this study, the association analysis of climate drought and a decline in groundwater level is addressed using Gaussian process classification (GPC) and backpropagation (BP) artificial neural network (ANN). This methodology is proposed to create a framework for decision making and reduce uncertainty in water resource management calculations, and in particular to optimize the management of groundwater drinking water sources.
Underground water levels in 609 study plains in Iran were used to predict drought over the test period, extending from 2017 to 2021. The artificial intelligence methods were implemented in the Python programming environment to achieve an annual precipitation rate. A statistical summary of the Rasterized Cells of the zoning maps was used to validate the prediction results. Considering the relationship between water quality reductions and drought in Iranian aquifers due to the occurrence of groundwater drought periods, the results were validated by analysis of the effect of climate drought using the Standardized Precipitation Index (SPI) on the occurrence of observed droughts with the Groundwater Resources Index (GRI). The results are well-illustrated by the observation of the predicted digits in the third dimension of the Gaussian distribution.
According to the SPI indicator, the southern regions of the country, and especially the central parts of the plain, can be considered the most affected areas by the most severe future droughts. The prediction results indicate a decrease in drought severity as part of a two-year sequence involving a recurrence of drought exacerbation and relative decline, as well as a failed state after the critical condition of aquifers.
地下水资源水平正在迅速变化，这就需要发现更新的地下水资源。干旱是影响人类生活和环境各个方面的最显著自然现象之一。在过去几十年中，人工智能技术的应用已被公认为预测年降水率的有效方法。
在本研究中，使用高斯过程分类（GPC）和反向传播（BP）人工神经网络（ANN）对气候干旱与地下水位下降进行关联分析。提出该方法是为了创建一个决策框架，减少水资源管理计算中的不确定性，特别是优化地下水饮用水源的管理。
利用伊朗609个研究平原的地下水位来预测2017年至2021年测试期内的干旱情况。在Python编程环境中实施人工智能方法以获得年降水率。使用分区图的栅格化单元格的统计摘要来验证预测结果。考虑到由于地下水干旱期的出现，伊朗含水层水质下降与干旱之间的关系，通过使用标准化降水指数（SPI）分析气候干旱对观测到的干旱发生与地下水资源指数（GRI）的影响来验证结果。高斯分布第三维中的预测数字观测很好地说明了结果。
根据SPI指标，该国南部地区，尤其是平原中部地区，可被视为未来最严重干旱影响最严重的地区。预测结果表明，作为干旱加剧和相对下降反复出现以及含水层临界状态后失效状态的两年序列的一部分，干旱严重程度有所下降。
