Enhancing water use efficiency in precision irrigation: data-driven approaches for addressing data gaps in time series

IF 2.6 Q2 WATER RESOURCES
Mohammad Zeynoddin, S. Gumiere, H. Bonakdari
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Abstract

Real-time soil matric potential measurements for determining potato production's water availability are currently used in precision irrigation. It is well known that managing irrigation based on soil matric potential (SMP) helps increase water use efficiency and reduce crop environmental impact. Yet, SMP monitoring presents challenges and sometimes leads to gaps in the collected data. This research sought to address these data gaps in the SMP time series. Using meteorological and field measurements, we developed a filtering and imputation algorithm by implementing three prominent predictive models in the algorithm to estimate missing values. Over 2 months, we gathered hourly SMP values from a field north of the Péribonka River in Lac-Saint-Jean, Québec, Canada. Our study evaluated various data input combinations, including only meteorological data, SMP measurements, or a mix of both. The Extreme Learning Machine (ELM) model proved the most effective among the tested models. It outperformed the k-Nearest Neighbors (kNN) model and the Evolutionary Optimized Inverse Distance Method (gaIDW). The ELM model, with five inputs comprising SMP measurements, achieved a correlation coefficient of 0.992, a root-mean-square error of 0.164 cm, a mean absolute error of 0.122 cm, and a Nash-Sutcliffe efficiency of 0.983. The ELM model requires at least five inputs to achieve the best results in the study context. These can be meteorological inputs like relative humidity, dew temperature, land inputs, or a combination of both. The results were within 5% of the best-performing input combination we identified earlier. To mitigate the computational demands of these models, a quicker baseline model can be used for initial input filtering. With this method, we expect the output from simpler models such as gaIDW and kNN to vary by no more than 20%. Nevertheless, this discrepancy can be efficiently managed by leveraging more sophisticated models.
提高精准灌溉用水效率:解决时间序列数据缺口的数据驱动方法
实时土壤基质势测量用于确定马铃薯生产的水分有效性,目前用于精确灌溉。众所周知,基于土壤基质潜力(SMP)管理灌溉有助于提高水分利用效率和减少作物对环境的影响。然而,SMP监测带来了挑战,有时会导致收集的数据存在空白。本研究试图解决这些数据缺口在SMP时间序列。利用气象和野外测量,我们开发了一种滤波和imputation算法,通过在算法中实现三个突出的预测模型来估计缺失值。在2个多月的时间里,我们收集了加拿大quacemenbecc - saint - jean的psamribonka河以北的一个油田的每小时SMP值。我们的研究评估了各种数据输入组合,包括仅气象数据、SMP测量或两者的混合。结果表明,极限学习机(ELM)模型是最有效的。它优于k近邻(kNN)模型和进化优化逆距离方法(gaIDW)。ELM模型的相关系数为0.992,均方根误差为0.164 cm,平均绝对误差为0.122 cm, Nash-Sutcliffe效率为0.983。ELM模型需要至少五个输入才能在研究环境中获得最佳结果。这些可以是气象输入,如相对湿度、露水温度、土地输入,或两者的组合。结果与我们之前确定的最佳输入组合相差不到5%。为了减轻这些模型的计算需求,可以使用更快的基线模型进行初始输入滤波。使用这种方法,我们期望简单模型(如gaIDW和kNN)的输出变化不超过20%。然而,这种差异可以通过利用更复杂的模型来有效地管理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Frontiers in Water
Frontiers in Water WATER RESOURCES-
CiteScore
4.00
自引率
6.90%
发文量
224
审稿时长
13 weeks
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