Computation of Evapotranspiration with Artificial Intelligence for Precision Water Resource Management
Computation of Evapotranspiration with Artificial Intelligence for Precision Water Resource Management
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DOI：
10.3390/app10051621
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发表时间：
2020-03-01
影响因子：
2.7
通讯作者：
Esau, Travis
中科院分区：
文献类型：
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作者：
Afzaal, Hassan;Farooque, Aitazaz A.;Esau, Travis
参考蒸散量的准确估算为水资源管理和农业可持续发展提供了有用的信息。本研究使用递归神经网络（RNN），即长短期记忆（LSTM）和双向LSTM来估计ETO。在加拿大的爱德华王子岛（PEI）选择了四个具有代表性的气象站点（北角、萨默赛德、哈灵顿和圣彼得），从四个站点的气候变量的平均值中形成PEI数据集，用于捕获该省所有地区的气候变化。基于子集回归分析，最高贡献的气候变量，即最高气温和相对湿度，被选为RNN训练（2011-2015）和测试（2016-2017）运行的输入变量。结果表明，LSTM和双向LSTM是准确（R-2 > 0.90）估计除哈灵顿外所有站点ETo的合适方法。测试期间（2016-2017年），所有研究中心记录的均方根误差范围为0.38-0.58 mm/天。在LSTM和双向LSTM的准确性方面没有观察到重大差异。这项研究的另一个目的是突出ETO和降雨之间的潜在差距，以评估农业可持续性在爱德华王子岛。对数据的分析突出表明，累积的ETO超过了累积的降雨量，可能影响到岛上主要作物的产量。因此，农业的可持续性需要可行的选择，如补充灌溉，以补充作物对水的需求。
Accurate estimation of reference evapotranspiration (ETo) provides useful information for water resource management and sustainable agriculture. This study estimates ETo with recurrent neural networks (RNNs), namely long short-term memory (LSTM) and bidirectional LSTM. Four representative meteorological sites (North Cape, Summerside, Harrington, and Saint Peters) were selected across Prince Edward Island (PEI), Canada to form a PEI dataset from mean values of the four sites' climatic variables for capturing climatic variability from all parts of the province. Based on subset regression analysis, the highest contributing climatic variables, namely maximum air temperature and relative humidity, were selected as input variables for RNNs' training (2011-2015) and testing (2016-2017) runs. The results suggested that the LSTM and bidirectional LSTM are suitable methods to accurately (R-2 > 0.90) estimate ETo for all sites except Harrington. Testing period (2016-2017) root mean square errors were recorded in range of 0.38-0.58 mm/day for all sites. No major differences were observed in accuracy of LSTM and bidirectional LSTM. Another objective of this study was to highlight the potential gap between ETO and rainfall for assessing agriculture sustainability in Prince Edward Island. Analyses of the data highlighted that the cumulative ETo surpassed the cumulative rainfall potentially affecting yield of major crops in the island. Therefore, agriculture sustainability requires viable options such as supplemental irrigation to replenish the crop water requirements as and when needed.