Comparing Machine Learning Models and Hybrid Geostatistical Methods Using Environmental and Soil Covariates for Soil pH Prediction
| Autor: | Ioannis Doukas, Maria Papadopoulou, Vassilis Aschonitis, Theocharis Chatzistathis, Panagiotis Tziachris |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
010504 meteorology & atmospheric sciences
Soil test Geography Planning and Development lcsh:G1-922 Geostatistics Machine learning computer.software_genre 01 natural sciences hybrid geostatistical methods Random search soil pH Kriging Linear regression Earth and Planetary Sciences (miscellaneous) geostatistics Computers in Earth Sciences 0105 earth and related environmental sciences Mathematics Hyperparameter business.industry 04 agricultural and veterinary sciences Random forest machine learning 040103 agronomy & agriculture 0401 agriculture forestry and fisheries Gradient boosting Artificial intelligence business computer lcsh:Geography (General) environmental variables |
| Zdroj: | ISPRS International Journal of Geo-Information, Vol 9, Iss 276, p 276 (2020) ISPRS International Journal of Geo-Information Volume 9 Issue 4 |
| ISSN: | 2220-9964 |
| Popis: | 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&rsquo 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&rsquo residuals. |
| Databáze: | OpenAIRE |
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