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Drought classification using gradient boosting decision tree
Acta Geophysica ( IF 2.3 ) Pub Date : 2021-04-24 , DOI: 10.1007/s11600-021-00584-8
Ali Danandeh Mehr

This paper compares the classification and prediction capabilities of decision tree (DT), genetic programming (GP), and gradient boosting decision tree (GBT) techniques for one-month ahead prediction of standardized precipitation index in Ankara province and standardized precipitation evaporation index in central Antalya region. The evolved models were developed based on multi-station prediction scenarios in which observed (reanalyzed) data from nearby stations (grid points) were used to predict drought conditions in a target location. To tackle the rare occurrence of extreme dry/wet conditions, the drought series at the target location was categorized into three classes of wet, normal, and dry events. The new models were trained and validated using the first 70% and last 30% of the datasets, respectively. The results demonstrated the promising performance of GBT for meteorological drought classification. It provides better performance than DT and GP in Ankara; however, GP predictions for Antalya were more accurate in the testing period. The results also exhibited that the proposed GP model with a scaled sigmoid function at root can effortlessly classify and predict the number of dry, normal, and wet events in both case studies.



中文翻译:

使用梯度增强决策树进行干旱分类

本文比较了决策树(DT),遗传规划(GP)和梯度提升决策树(GBT)技术对安卡拉省标准化降水指数和中部地区降水降水指数提前一个月预测的分类和预测能力安塔利亚地区。基于多站预测方案开发了演化模型,其中使用了来自附近站(网格点)的观测(重新分析)数据来预测目标位置的干旱状况。为了解决极端干燥/潮湿条件的罕见情况,将目标位置的干旱序列分为湿,正常和干燥事件三类。分别使用数据集的前70%和后30%对新模型进行了训练和验证。结果表明,GBT在气象干旱分类方面具有令人鼓舞的性能。它提供了比安卡拉的DT和GP更好的性能;但是,在测试期间,对安塔利亚的GP预测更为准确。结果还表明,在两个案例研究中,提出的具有根状乙状结肠功能的GP模型都可以轻松分类和预测干燥,正常和潮湿事件的数量。

更新日期:2021-04-24
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