Evaluation of soft-computing techniques for pan evaporation estimation

Abstract

Estimation of pan evaporation (Epan)  can be useful in judicious irrigation scheduling for enhancing agricultural water productivity. The aim of  present study was to assess the efficacy of state-of-the-art LSTM and ANN for daily Epan estimation using meteorological data. Besides this, the effect of static time-series (Julian date) as additional input variable was investigated on performance of soft-computing techniques. For this purpose,the models were trained, tested and validated with eight meteorological variables of 37 years by using preceding 1-, 3- and 5- days’ information. Data were partitioned into three groups as training (60%), testing (20%), and validation (20%) components. It was observed that the models performed well (best) with preceding 5-days meteorological information followed by 3-days and 1-day. However, all LSTMs simulated peak value of Epan was more accurate as compared to lower values. Meteorological data with julian date improved the performance of LSTMs (0.75

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评估平底锅蒸发量估算的软计算技术

估算盘面蒸发量（Epan）有助于制定明智的灌溉计划，提高农业用水生产率。本研究旨在评估最先进的 LSTM 和 ANN 在利用气象数据估算日蒸发量方面的功效。此外，还研究了静态时间序列（朱利安日期）作为附加输入变量对软计算技术性能的影响。为此，利用前 1 天、3 天和 5 天的信息，用 37 年的 8 个气象变量对模型进行了训练、测试和验证。数据被分为三组，即训练（60%）、测试（20%）和验证（20%）部分。结果表明，模型在使用前 5 天气象信息时表现良好（最佳），其次是 3 天和 1 天。不过，与较低值相比，所有 LSTM 模拟的 Epan 峰值都更准确。带有朱利安日期的气象数据提高了 LSTM 的性能（0.75

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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