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Modeling Short Term Rainfall Forecast Using Neural Networks, and Gaussian Process Classification Based on the SPI Drought Index
Water Resources Management ( IF 4.3 ) Pub Date : 2020-02-16 , DOI: 10.1007/s11269-020-02507-6
Saeed Azimi , Mehdi Azhdary Moghaddam

Abstract

Determining the quantitative occurrence of droughts, discovering the spatial correlation of droughts, and predicting the occurrence of undesirable classes of drinking water quality and aquifer farming is of high importance. In this research, the Standardized Precipitation Index (SPI) was calculated and analyzed with a monthly survey of 26,027 wells in 609 study areas over a period of 20 years. After analyzing the missing data, the annual rainfall was forecasted in 362 synoptic stations of the country based on an artificial intelligence model. In addition, statistical relationships were extracted in order to achieve a comprehensive and historical map of the state of shortages and surpluses of water resources, as well as verification of artificial intelligence relationships in predicting base data cultivars. The results indicated that the “mild drought” indicator was steeper than the “near-normal drought” indicator. Eventually, the southern and eastern regions and certain parts of the northeast of the country in the period from 2005 to 2015 were placed in the 7th and 8th classes, which indicates severe drought. The analysis of the period 1994–2014 showed that the plains of the Sistan and Baluchestan Province in the south-east region of the country have been significantly more affected by the droughts. With the exception of the central parts of Khorasan, the general eastern, southeast, and southern regions of the country can be considered as an absolute drought class for the long term.



中文翻译:

使用神经网络对短期降雨预测进行建模,并基于SPI干旱指数进行高斯过程分类

摘要

确定干旱的定量发生,发现干旱的空间相关性以及预测不良的饮用水质量和含水层耕种的发生非常重要。在这项研究中,通过计算在20年内每月对609个研究区域中的26,027口井进行了调查,计算并分析了标准化降水指数(SPI)。在分析了缺失的数据之后,基于人工智能模型,对该国362个天气站的年降雨量进行了预报。此外,还提取了统计关系,以便获得水资源短缺和过剩状态的全面历史地图,并在预测基础数据品种时验证人工智能关系。结果表明,“轻度干旱”指标比“接近正常干旱”指标陡峭。最终,从2005年到2015年,该国的南部和东部地区以及东北部的某些地区被划分为第7级和第8级,这表明干旱严重。对1994-2014年期间的分析表明,该国东南地区的锡斯坦和and路支斯坦省平原受到干旱的影响更大。除了霍拉桑省的中部地区外,该国的东部,东南部和南部地区可长期视为绝对干旱类别。2005年至2015年期间,该国的南部和东部地区以及东北部的某些地区被划分为第7级和第8级,这表明干旱严重。对1994-2014年期间的分析表明,该国东南部地区的锡斯坦和Bal路支斯坦省平原受干旱的影响更大。除了霍拉桑省的中部地区外,该国的东部,东南部和南部地区可长期视为绝对干旱。2005年至2015年期间,该国的南部和东部地区以及东北部的某些地区被划分为第7级和第8级,这表明干旱严重。对1994-2014年期间的分析表明,该国东南地区的锡斯坦和and路支斯坦省平原受到干旱的影响更大。除了霍拉桑省的中部地区外,该国的东部,东南部和南部地区可长期视为绝对干旱类别。

更新日期:2020-03-20
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