Light Gradient Boosting Machine: An efficient soft computing model for estimating daily reference evapotranspiration with local and external meteorological data
Light Gradient Boosting Machine: An efficient soft computing model for estimating daily reference evapotranspiration with local and external meteorological data
复制标题
光梯度增强机：一种高效的软计算模型，用于利用本地和外部气象数据估算每日参考蒸散量
DOI：
10.1016/j.agwat.2019.105758
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发表时间：
2019-11-20
影响因子：
6.7
通讯作者：
Zeng, Wenzhi
中科院分区：
文献类型：
--
作者：
Fan, Junliang;Ma, Xin;Zeng, Wenzhi
在灌溉制度设计、农业用水管理、作物生长模拟和干旱评估等许多领域，都需要准确估计参考蒸散量(ETO)。然而，当目标站缺乏完整或长期的气象数据时，很难可靠地估计ETO。利用中国湿润亚热带地区49个气象站(江西16个，江西33个)的有限局地(目标站)和外部(跨站)气象资料，对一种新的基于树状结构的软计算模型--光梯度增强机(LightGBM)估算逐日ETO的有效性进行了评估。LightGBM的性能与基于树的M5模型树(M5Tree)和随机森林(RF)以及四个经验模型(Hargreaves-Samani，Tabari，Makkink和Trabert)进行了比较。利用由日照时数(N)计算的最高气温(T-max)、最低气温(T-min)、相对湿度(H-r)、2m高空风速(U-2)、地外太阳辐射(R-a)和总太阳辐射(R-S)等8种输入组合对模型进行检验。结果表明，在所有输入组合下，LightGBM的局部应用效果均优于M5Tree和RF，平均均方根误差分别为0.08~0.58 mm d(-1)、0.11~0.62 mm d(-1)和0.13~0.60 mm d(-1)。在输入组合6-8下，M5Tree的表现略好于RF，而在其他输入组合下，RF的表现略好于M5Tree。然而，在输入变量相同的情况下，三种软计算模型得出的每日ETO估计值都比相应的经验模型要好得多。对该地区逐日ETO估算影响最大的是R-S，其次是T-max、T-min、H-r，最后是U-2。在外部应用中，LightGBM通常也比RF、M5Tree和经验模型表现得更好。用57894站的气象资料建立的软计算模式，对江西省的15个交叉站，甚至对中国湿润的亚热带地区的其他33个站，给出了令人满意的ETO估计，该模式与其他站的气候特征最为相似。LightGBM被证明是有效的，并在本地和外部应用中都表现出良好的泛化能力，因此被推荐作为每日ETO估计的替代模型。
Accurate estimation of reference evapotranspiration (ETo) is required in many fields, e.g. irrigation scheduling design, agricultural water management, crop growth modeling and drought assessment. Nevertheless, reliable estimation of ETo is difficult when lack of complete or long-term meteorological data at the target station. This study evaluated the efficiency of a new tree-based soft computing model, Light Gradient Boosting Machine (LightGBM), for estimating daily ETo using limited local (target-station) and external (cross-station) meteorological data from 49 weather stations in humid subtropical region of China, including 16 in Jiangxi Province and other 33 in the region. The performance of LightGBM was compared with the tree-based M5 Model Tree (M5Tree) and Random Forests (RF) as well as four empirical models (Hargreaves-Samani, Tabari, Makkink and Trabert). Eight input combinations of daily meteorological data including maximum temperature (T-max), minimum temperature (T-min), relative humidity (H-r), wind speed at 2 m height (U-2), extraterrestrial solar radiation (R-a) and global solar radiation (R-s) calculated from sunshine duration (n) for the period 2001-2015 were used to test the models. The results showed that LightGBM was superior to M5Tree and RF in local applications under all input combinations during testing, with average root mean square error (RMSE) of 0.08-0.58 mm d(-1), 0.11-0.62 mm d(-1) and 0.13-0.60 mm d(-1), respectively. M5Tree performed slightly better than RF under input combinations 6-8, whereas RF outperformed M5Tree under the other input combinations. However, all three soft computing models produced much better daily ETo estimates than the corresponding empirical models with the same input variables. R-s was the most influential meteorological variable for daily ETo estimation in this region, followed by T-max and T-min, H-r and finally U-2. In external applications, LightGBM also generally performed better than the RF, M5Tree and empirical models. Soft computing models developed with meteorological data from Station 57894, having the most similar climatic characteristics to the other stations, gave satisfactory ETo estimates for the 15 cross stations in Jiangxi Province, even for the other 33 stations across the humid subtropical region of China. LightGBM was proved to be efficient and exhibit good generalization capability in both local and external applications, which was thus recommended as an alternative model for daily ETo estimation.