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首页> 外文期刊>Journal of Hydroinformatics >Nitrate leaching from a potato field using adaptive network-based fuzzy inference system
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Nitrate leaching from a potato field using adaptive network-based fuzzy inference system

机译：基于自适应网络模糊推理系统的马铃薯田硝酸盐淋失

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摘要

The conventional methods of application of nitrogen fertilizers might be responsible for the increased nitrate concentration in groundwater of areas dominated by irrigated agriculture. Appropriate water and nutrient management strategies are required to minimize groundwater pollution and to maximize nutrient use efficiency and production. Design and operation of a drip fertigation system requires understanding of nutrient leaching behavior in cases of shallow rooted crops such as potatoes which cannot extract nutrient from a lower soil depth. This study deals with neuro-fuzzy modeling of nitrate (NO_3~-) leaching from a potato field under a drip fertigation system, in the first part of the study, a two-dimensional solute transport model was used to simulate nitrate leaching from a sandy soil with varying emitter discharge rates and fertilizer doses. The results from the modeling were used to train and validate an adaptive network-based fuzzy inference system (ANFIS) in order to estimate nitrate leaching. Two performance functions, namely mean absolute percentage error (MAPE) and correlation coefficient (R), were used to evaluate the adequacy of the ANFIS. Results showed that ANFIS can accurately simulate HYDRUS-2D behavior regarding nitrate leaching under the circumstances of the present study.
机译：常规氮肥施用方法可能是灌溉农业占主导的地区地下水硝酸盐浓度升高的原因。需要采取适当的水和养分管理策略，以最大程度减少地下水污染，并最大限度地提高养分利用效率和产量。滴灌施肥系统的设计和操作需要了解在马铃薯等根系较浅的作物无法从较低土壤深度提取养分的情况下养分的淋溶行为。本研究涉及滴灌施肥系统下马铃薯田中硝酸盐（NO_3〜-）淋溶的神经模糊建模，在研究的第一部分中，使用二维溶质运移模型来模拟沙质土壤中硝酸盐的淋溶。发射器排放速率和肥料剂量不同的土壤。建模的结果用于训练和验证基于自适应网络的模糊推理系统（ANFIS），以估算硝酸盐的浸出量。两个性能函数，即平均绝对百分比误差（MAPE）和相关系数（R），用于评估ANFIS的适当性。结果表明，在本研究的情况下，ANFIS可以准确模拟HYDRUS-2D与硝酸盐浸出有关的行为。

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