用机器学习算法数值估算不同气候类型下的地表土壤湿度

IF 1.9 4区 地球科学 Q2 GEOCHEMISTRY & GEOPHYSICS pure and applied geophysics Pub Date : 2024-05-29 DOI:10.1007/s00024-024-03508-x
Sadaf Ahmadnejad, Mehdi Nadi, Pouya Aghelpour
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引用次数: 0

摘要

本研究旨在提供地表土壤水分数值估算模型。该评估是基于使用机器学习模型对 5 厘米(SM5)和 10 厘米(SM10)深度的土壤水分进行的直接地面测量。为此,使用了各种气象变量(16 个变量)作为模型输入。对 2017-2020 年期间的数据进行了逐日评估。在这些数据中,75% 的天数被随机视为训练天数,25% 的天数被视为测试天数。与空气和土壤温度、相对空气湿度、蒸发和蒸汽压相关的成分是影响每日土壤湿度的最重要因素。这些变量的混合物被用作模型输入。为此,使用了两种机器学习模型,包括多层感知器(MLP)神经网络和自适应神经模糊推理系统(ANFIS)。对位于三种不同气候条件下的三个农业气象站进行了评估:(1) Gharakhil 站(半湿润、温和)、Zarghan 站(半干旱、寒冷)和 Zahak 站(特干旱、温和)。根据估算值与实测值之间的比较,两个模型在加拉希尔和扎尔汗的表现都比较理想(SM5 为 57%<R2<66%,SM10 为 45%<R2<58%)。然而,在极端干旱的扎哈克气候条件下,这些数据的表现却很微弱,几乎不可接受(SM5 为 14% <R2<17%,SM10 为 18% <R2<22%)。根据各站的相对均方根误差(RRMSE)和 Nash-Sutcliffe 值,湿润气候下的模型比干旱和特干旱气候下的模型表现更好。在 Gharakhil 站,通过 ANFIS 获得的 RRMSE 值最好(SM5 为 0.193,SM10 为 0.178),而在 Zahak 站获得的 RRMSE 值最弱,SM5 和 SM10 分别为 0.887(通过 MLP)和 0.767(通过 ANFIS)。所应用的模型之间并无优劣之分;不过,在大多数情况下,ANFIS 模型略优于 MLP。
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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Numerical Estimation of Surface Soil Moisture by Machine Learning Algorithms in Different Climatic Types

The present study was designed to provide a model for surface soil moisture numerical estimation. This assessment is done based on the direct ground measurement of soil moisture in 5 cm (SM5) and 10 cm (SM10) depths using machine learning models. To do this, various meteorological variables (16 variables) were used as model inputs. The data were evaluated on a daily scale during 2017–2020. Of these data, 75% of days were randomly considered as train and 25% as test. The components relevant to air and soil temperature, relative air humidity, evaporation, and vapor pressure are the most important factors that affect daily soil moisture. A mixture of these variables is used as model input. For this purpose, two machine learning models, including a multilayer perceptron (MLP) neural network and an adaptive neuro-fuzzy inference system (ANFIS) were used. Three agriculture meteoritical stations located in three different climates were assessed: (1) Gharakhil Station (semi-humid and moderate), Zarghan Station (semi-arid and cold), and Zahak Station (extra-arid and moderate). According to the comparison between estimates and measurements, both models had a relatively desired performance in Gharakhil and Zarghan (57% < R2 < 66% for SM5 and 45% < R2 < 58% for SM10). However, the performances were weak and almost unacceptable in the extra-arid Zahak climate (14% < R2 < 17% for SM5 and 18% < R2 < 22% for SM10). According to the relative root mean square error (RRMSE) and Nash–Sutcliffe value of stations, the models in humid climates, performed better than those in arid and extra-arid climates. The best RRMSE value was obtained by ANFIS in Gharakhil Stations (0.193 for SM5 and 0.178 for SM10), while the weakest RRMSE value was obtained in Zahak Station, which equaled 0.887 (via MLP) and 0.767 (via ANFIS) for SM5 and SM10, respectively. The applied models were not superior to each other; however, the ANFIS model was slightly superior to MLP in most cases.

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来源期刊
pure and applied geophysics
pure and applied geophysics 地学-地球化学与地球物理
CiteScore
4.20
自引率
5.00%
发文量
240
审稿时长
9.8 months
期刊介绍: pure and applied geophysics (pageoph), a continuation of the journal "Geofisica pura e applicata", publishes original scientific contributions in the fields of solid Earth, atmospheric and oceanic sciences. Regular and special issues feature thought-provoking reports on active areas of current research and state-of-the-art surveys. Long running journal, founded in 1939 as Geofisica pura e applicata Publishes peer-reviewed original scientific contributions and state-of-the-art surveys in solid earth and atmospheric sciences Features thought-provoking reports on active areas of current research and is a major source for publications on tsunami research Coverage extends to research topics in oceanic sciences See Instructions for Authors on the right hand side.
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