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Drought prediction models driven by meteorological and remote sensing data in Guanzhong Area, China
Hydrology Research ( IF 2.7 ) Pub Date : 2020-06-23 , DOI: 10.2166/nh.2020.184
Jianzhu Li 1 , Siyao Zhang 1 , Lingmei Huang 2 , Ting Zhang 1 , Ping Feng 1
Affiliation  

Drought is an important factor that limits economic and social development due to its frequent occurrence and profound influence. Therefore, it is of great significance to make accurate predictions of drought for early warning and disaster alleviation. In this paper, SPEI-1 was confirmed to classify drought grades in theGuanzhongArea, and theautoregressive integratedmoving average (ARIMA), random forest (RF) and support vectormachine (SVM)model were established. Meteorological data and remote sensing datawereused to derive thepredictionmodels. The results showed that (1) the SVMmodel performed the best when the models were developed using meteorological data, remote sensing data and a combination ofmeteorological and remote sensing data, but themodel’s corresponding kernel functions aredifferent and include linear, polynomial andGaussian radial basiskernel functions, respectively. (2) The RF model driven by the remote sensing data and the SVMmodel driven by the combinedmeteorological and remote sensing datawere found to perform better than themodel driven by the corresponding other data in the Guanzhong Area. It is difficult to accurately measure drought with the single meteorological data. Only by considering the combined factors canwemore accuratelymonitor and predict drought. This study can provide an important scientific basis for regional drought warnings and predictions.

中文翻译:

关中地区气象遥感数据驱动的干旱预测模型

干旱频发,影响深远,是制约经济社会发展的重要因素。因此,准确预测干旱对预警和减灾具有重要意义。本文证实了SPEI-1对关中地区干旱等级进行分类,并建立了自回归综合移动平均(ARIMA)、随机森林(RF)和支持向量机(SVM)模型。气象数据和遥感数据用于推导预测模型。结果表明:(1)SVM模型在使用气象数据、遥感数据以及气象遥感数据组合开发模型时表现最好,但模型对应的核函数不同,包括线性、多项式和高斯径向基核函数,分别。(2)在关中地区,遥感数据驱动的RF模型和气象遥感数据驱动的SVM模型的性能优于其他相应数据驱动的模型。单一的气象数据难以准确衡量干旱。只有综合考虑综合因素,才能更准确地监测和预测干旱。该研究可为区域干旱预警预报提供重要的科学依据。单一的气象数据难以准确衡量干旱。只有综合考虑综合因素,才能更准确地监测和预测干旱。该研究可为区域干旱预警预报提供重要的科学依据。单一的气象数据难以准确衡量干旱。只有综合考虑综合因素,才能更准确地监测和预测干旱。该研究可为区域干旱预警预报提供重要的科学依据。
更新日期:2020-06-23
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