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Open AccessArticle

Comparing Machine Learning Models and Hybrid Geostatistical Methods Using Environmental and Soil Covariates for Soil pH Prediction

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Soil and Water Resources Institute, Hellenic Agricultural Organization (H.A.O.)-DEMETER, 570 01 Thessaloniki, Greece
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Department of Cadastre, Photogrammetry and Cartography, Faculty of Engineering, Aristotle University of Thessaloniki (AUTH), 541 24 Thessaloniki, Greece
3
School of Civil Engineering, Faculty of Engineering, Aristotle University of Thessaloniki (AUTH), 541 24 Thessaloniki, Greece
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2020, 9(4), 276; https://doi.org/10.3390/ijgi9040276
Received: 28 February 2020 / Revised: 29 March 2020 / Accepted: 16 April 2020 / Published: 23 April 2020
In the current paper we assess different machine learning (ML) models and hybrid geostatistical methods in the prediction of soil pH using digital elevation model derivates (environmental covariates) and co-located soil parameters (soil covariates). The study was located in the area of Grevena, Greece, where 266 disturbed soil samples were collected from randomly selected locations and analyzed in the laboratory of the Soil and Water Resources Institute. The different models that were assessed were random forests (RF), random forests kriging (RFK), gradient boosting (GB), gradient boosting kriging (GBK), neural networks (NN), and neural networks kriging (NNK) and finally, multiple linear regression (MLR), ordinary kriging (OK), and regression kriging (RK) that although they are not ML models, they were used for comparison reasons. Both the GB and RF models presented the best results in the study, with NN a close second. The introduction of OK to the ML models’ residuals did not have a major impact. Classical geostatistical or hybrid geostatistical methods without ML (OK, MLR, and RK) exhibited worse prediction accuracy compared to the models that included ML. Furthermore, different implementations (methods and packages) of the same ML models were also assessed. Regarding RF and GB, the different implementations that were applied (ranger-ranger, randomForest-rf, xgboost-xgbTree, xgboost-xgbDART) led to similar results, whereas in NN, the differences between the implementations used (nnet-nnet and nnet-avNNet) were more distinct. Finally, ML models tuned through a random search optimization method were compared with the same ML models with their default values. The results showed that the predictions were improved by the optimization process only where the ML algorithms demanded a large number of hyperparameters that needed tuning and there was a significant difference between the default values and the optimized ones, like in the case of GB and NN, but not in RF. In general, the current study concluded that although RF and GB presented approximately the same prediction accuracy, RF had more consistent results, regardless of different packages, different hyperparameter selection methods, or even the inclusion of OK in the ML models’ residuals. View Full-Text
Keywords: machine learning; geostatistics; hybrid geostatistical methods; soil pH; environmental variables machine learning; geostatistics; hybrid geostatistical methods; soil pH; environmental variables
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Tziachris, P.; Aschonitis, V.; Chatzistathis, T.; Papadopoulou, M.; Doukas, I.J.D. Comparing Machine Learning Models and Hybrid Geostatistical Methods Using Environmental and Soil Covariates for Soil pH Prediction. ISPRS Int. J. Geo-Inf. 2020, 9, 276.

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