Dynamic Neural Network Modelling of Soil Moisture Content for Predictive Irrigation Scheduling.
Dynamic Neural Network Modelling of Soil Moisture Content for Predictive Irrigation Scheduling.
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DOI：
10.3390/s18103408
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发表时间：
2018-10-11
期刊：
影响因子：
--
通讯作者：
Norton T
中科院分区：
文献类型：
--
作者：
Adeyemi O;Grove I;Peets S;Domun Y;Norton T
可持续淡水管理的基础是提高农业灌溉系统效率的技术。灌溉调度有可能纳入土壤湿度和气候传感器的实时反馈。然而，对于强大的闭环决策支持，土壤水分动态模型是必不可少的，以预测作物水分需求，同时适应外部扰动和干扰。本文提出了一种动态神经网络方法来模拟土壤水分通量的时间。这些模型经过训练，可以根据过去的土壤水分、降水和气候测量结果，提前一天预测土壤体积含水量。使用来自三个站点的现场数据，在所有站点的模型评估期间获得了0.94以上的值。这些模型还能够为独立的站点生成强大的土壤水分预测，这些站点没有用于训练模型。应用动态神经网络模型在预测灌溉调度系统中进行了演示，使用AQUACROP模拟马铃薯生长季节。预测灌溉调度系统进行了评估，对基于规则的系统，适用于灌溉的基础上预定义的阈值。结果表明，预测系统实现了节水20%至46%，同时实现了类似的基于规则的系统的产量和水分利用效率。
Sustainable freshwater management is underpinned by technologies which improve the efficiency of agricultural irrigation systems. Irrigation scheduling has the potential to incorporate real-time feedback from soil moisture and climatic sensors. However, for robust closed-loop decision support, models of the soil moisture dynamics are essential in order to predict crop water needs while adapting to external perturbation and disturbances. This paper presents a Dynamic Neural Network approach for modelling of the temporal soil moisture fluxes. The models are trained to generate a one-day-ahead prediction of the volumetric soil moisture content based on past soil moisture, precipitation, and climatic measurements. Using field data from three sites, a value above 0.94 was obtained during model evaluation in all sites. The models were also able to generate robust soil moisture predictions for independent sites which were not used in training the models. The application of the Dynamic Neural Network models in a predictive irrigation scheduling system was demonstrated using AQUACROP simulations of the potato-growing season. The predictive irrigation scheduling system was evaluated against a rule-based system that applies irrigation based on predefined thresholds. Results indicate that the predictive system achieves a water saving ranging between 20 and 46% while realizing a yield and water use efficiency similar to that of the rule-based system.
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影响因子：
6.7
作者：
Garcia Morillo, J.;Martin, M.;Montesinos, P.
通讯作者：
Montesinos, P.
影响因子：
5.1
作者：
Chai, T.;Draxler, R. R.
通讯作者：
Draxler, R. R.
影响因子：
7.8
作者：
FUNAHASHI, K;NAKAMURA, Y
通讯作者：
NAKAMURA, Y
DOI：
10.1111/j.2517-6161.1964.tb00553.x
发表时间：
1964-01-01
影响因子：
5.8
作者：
BOX, GEP;COX, DR
通讯作者：
COX, DR
DOI：
10.4236/jwarp.2012.410105
发表时间：
2012-01-01
期刊：
Journal of Water Resource and Protection
影响因子：
--
作者：
Archana Sarkar, Archana Sarkar;Rakesh Kumar, Rakesh Kumar;Kumar, R.
通讯作者：
Kumar, R.