Model ensemble techniques of machine learning algorithms for soil moisture constants in the semi-arid climate conditions

被引:0
|
作者
Alaboz, Pelin [1 ]
机构
[1] Isparta Univ Appl Sci, Dept Soil Sci & Plant Nutr, Isparta, Turkiye
关键词
model average; pedotransfer functions; soil water; eau du sol; moyenne du mod & egrave; le; fonctions de p & eacute; dotransfert; POINT;
D O I
10.1002/ird.3037
中图分类号
S3 [农学(农艺学)];
学科分类号
0901 ;
摘要
In recent years, the use of prediction models based on intelligent algorithms has become widespread in soil science. However, each algorithm has advantages and disadvantages, and variable results can occur on different datasets. The evaluation of ensemble techniques for solving these problems is the current approach. Water problems will arise due to global warming, and soil water will become more important. This study aims to evaluate the predictive accuracy of different machine learning algorithms (support vector machine regression (SVR), random forest (RF), artificial neural network (ANN), and multivariate linear regression (MLR)) and ensemble techniques (equal weight [EQ], Bates-Granger-BG), Granger-Ramanathan (GR), Akaike information criterion (AIC), and Bayesian information criterion (BIC)) on the field capacity (FC), wilting point (WP) and available water content (AWC) of soils. As a result, higher prediction accuracy was obtained with the RF algorithm than with the value machine learning algorithm in the estimation of moisture constants. The coefficients of determination (R2) obtained for the prediction of FC, WP, and AWC via the RF algorithms were 0.624, 0.759 and 0.641, respectively. MLR had the highest error rate. Among the ensemble techniques, GR was the most successful. Lin's concordance correlation coefficient (LCCC) values obtained from the estimation of FC, WP, and AWC with the GR model were 0.801, 0.894, and 0.801, respectively. The root mean squared error (RMSE) and mean absolute error (MAE) values obtained in the estimation of the available water content with the MLR algorithm were 1.905 and 1.435, respectively, whereas these values were 1.173 and 0.767, respectively, when the GR model was used. As a result of the present study, better predictive results were obtained with ensemble techniques instead of evaluating the algorithms individually. Ces derni & egrave;res ann & eacute;es, l'utilization de mod & egrave;les de pr & eacute;diction bas & eacute;s sur des algorithmes intelligents s'est r & eacute;pandue dans la science des sols. Cependant, chaque algorithme pr & eacute;sente des avantages et des inconv & eacute;nients, et des r & eacute;sultats variables peuvent se produire sur diff & eacute;rents ensembles de donn & eacute;es. L'approche actuelle est d'& eacute;valuer des techniques d'ensemble pour r & eacute;soudre ces probl & egrave;mes. Les probl & egrave;mes d'eau vont appara & icirc;tre en raison du r & eacute;chauffement climatique, et l'eau du sol va devenir plus importante. Cette & eacute;tude vise & agrave; & eacute;valuer la pr & eacute;cision pr & eacute;dictive de diff & eacute;rents algorithmes d'apprentissage automatique (r & eacute;gression & agrave; vecteurs de support (SVR), for & ecirc;t al & eacute;atoire (RF), r & eacute;seau de neurones artificiels (ANN) et r & eacute;gression lin & eacute;aire multivari & eacute;e (MLR)) et techniques d'ensemble (poids & eacute;gal [EQ], Bates-Granger-BG), Granger-Ramanathan (GR), crit & egrave;re d'information d'Akaike (AIC) et crit & egrave;re d'information bay & eacute;sien (BIC)) sur la capacit & eacute; au champ (FC), le point de fl & eacute;trissement (WP) et la teneur en eau disponible (AWC) des sols. En cons & eacute;quence, une pr & eacute;cision de pr & eacute;diction plus & eacute;lev & eacute;e a & eacute;t & eacute; obtenue avec l'algorithme RF qu'avec l'algorithme d'apprentissage automatique des valeurs pour l'estimation des constantes d'humidit & eacute;. Les coefficients de d & eacute;termination (R2) obtenus pour la pr & eacute;diction de FC, WP et AWC au moyen des algorithmes RF & eacute;taient de 0,624, 0,759 et 0,641 respectivement. La MLR affichait le taux d'erreur le plus & eacute;lev & eacute;. Parmi les techniques d'ensemble, le GR a eu le plus de succ & egrave;s. Les valeurs du coefficient de corr & eacute;lation de concordance de Lin (LCCC) obtenues & agrave; partir de l'estimation de FC, WP et AWC avec le mod & egrave;le GR & eacute;taient de 0,801, 0,894 et 0,801, respectivement. Les valeurs de l'erreur quadratique moyenne (RMSE) et de l'erreur absolue moyenne (MAE) obtenues pour l'estimation de la teneur en eau disponible avec l'algorithme MLR & eacute;taient de 1,905 et 1,435 respectivement, alors que ces valeurs & eacute;taient de 1,173 et 0,767 respectivement, lorsque le mod & egrave;le GR a & eacute;t & eacute; utilis & eacute;. & Agrave; la suite de la pr & eacute;sente & eacute;tude, de meilleurs r & eacute;sultats pr & eacute;dictifs ont & eacute;t & eacute; obtenus avec des techniques d'ensemble au lieu d'& eacute;valuer les algorithmes s & eacute;par & eacute;ment.
引用
收藏
页数:12
相关论文
共 50 条
  • [31] The impacts of assimilating satellite soil moisture into a rainfall-runoff model in a semi-arid catchment
    Alvarez-Garreton, C.
    Ryu, D.
    Western, A. W.
    Crow, W. T.
    Robertson, D. E.
    JOURNAL OF HYDROLOGY, 2014, 519 : 2763 - 2774
  • [32] Surface Soil Moisture Temporal Persistence and Stability in a Semi-Arid Watershed
    Cosh, Michael H.
    Jackson, Thomas J.
    Moran, Susan
    Bindlish, Rajat
    2006 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, VOLS 1-8, 2006, : 1724 - +
  • [33] Soil moisture controls on phenology and productivity in a semi-arid critical zone
    Cleverly, James
    Eamus, Derek
    Coupe, Natalia Restrepo
    Chen, Chao
    Maes, Wouter H.
    Li, Longhui
    Faux, Ralph
    Santini, Nadia S.
    Rumman, Rizwana
    Yu, Qiang
    Huete, Alfredo
    SCIENCE OF THE TOTAL ENVIRONMENT, 2016, 568 : 1227 - 1237
  • [34] 'Perola' pineapple growth under semi-arid climate conditions
    Maia, V. M.
    Oliveira, F. S.
    Pegoraro, R. F.
    Aspiazu, I.
    Pereira, M. C. T.
    XXIX INTERNATIONAL HORTICULTURAL CONGRESS ON HORTICULTURE: SUSTAINING LIVES, LIVELIHOODS AND LANDSCAPES (IHC2014): IV INTERNATIONAL SYMPOSIUM ON PAPAYA, VIII INTERNATIONAL PINEAPPLE SYMPOSIUM, AND INTERNATIONAL SYMPOSIUM ON MANGO, 2016, 1111 : 267 - 273
  • [35] Advanced machine learning-based kharif maize evapotranspiration estimation in semi-arid climate
    Jatav, Malkhan Singh
    Sarangi, A.
    Singh, D. K.
    Sahoo, R. N.
    Varghese, Cini
    WATER SCIENCE AND TECHNOLOGY, 2023, 88 (04) : 991 - 1014
  • [36] The imprint of coevolving semi-arid landscapes, soil, and vegetation on soil moisture and vegetation variability
    Srivastava, Ankur
    Yetemen, Omer
    Rodriguez, Jose F.
    Kumari, Nikul
    Saco, Patricia M.
    CATENA, 2024, 242
  • [37] Deep soil moisture limits the sustainable vegetation restoration in arid and semi-arid Loess Plateau
    Li, Bin-Bin
    Li, Pan-Pan
    Zhang, Wan-Tao
    Ji, Jing-Yi
    Liu, Guo-Bin
    Xu, Ming-Xiang
    GEODERMA, 2021, 399
  • [38] Influences of afforestation policies on soil moisture content in China's arid and semi-arid regions
    Cao, Jianjun
    Tian, Hong
    Adamowski, Jan F.
    Zhang, Xiaofang
    Cao, Zijian
    LAND USE POLICY, 2018, 75 : 449 - 458
  • [39] A Review on Soil Moisture Dynamics Monitoring in Semi-Arid Ecosystems: Methods, Techniques, and Tools Applied at Different Scales
    Duarte, Efrain
    Hernandez, Alexander
    APPLIED SCIENCES-BASEL, 2024, 14 (17):
  • [40] Soil carbon prediction under different land uses by integration of remote sensing and machine learning algorithms in a semi-arid watershed, Iran
    Mohammad Tahmoures
    Samira Mesri
    Banafsheh Afrasiabi
    Afshin Honarbakhsh
    Ben Ingram
    Arabian Journal of Geosciences, 2023, 16 (1)