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

Panagiotis Tziachris,Maria Papadopoulou,Theocharis Chatzistathis,Vassilis Aschonitis,Ioannis (John) D Doukas

doi:10.3390/ijgi9040276

Panagiotis Tziachris, Maria Papadopoulou + Show 3 more

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https://doi.org/10.3390/ijgi9040276

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Abstract

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.

Highlights

Environmental sciences have always been interested in accurately predicting the spatial distributions of different phenomena regarding soil, water, air, etc. [1,2,3,4]
The results showed that the predictions were improved by the optimization process only where the machine learning (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 gradient boosting (GB) and neural networks (NN), but not in random forests (RF)
From the results of the current study, it is obvious that ML models outperformed the other methods in predicting soil pH, like multiple linear regression (MLR) or models that use kriging (RK or ordinary kriging (OK))

Summary

Introduction

Environmental sciences have always been interested in accurately predicting the spatial distributions of different phenomena regarding soil, water, air, etc. [1,2,3,4]. The increased numbers of digital data (Internet of Things, high-accuracy digital elevation models (DEM), satellite images) present a great opportunity for improved prediction results. The prediction of spatial phenomena was achieved with the use of spatial prediction methods which mainly fell into the following two categories: deterministic methods, like inverse distance weighting or nearest neighbors, and stochastic ones, like regression models and kriging variations (e.g., ordinary kriging, universal kriging, etc.). Hybrid methods were introduced [5,6,7] that were partially deterministic, partially stochastic, like regression kriging (RK) or kriging with external drift (KED). These methods tried to combine the advantages of both worlds, deterministic and stochastic, achieving improved results. More innovative implementations of the abovementioned hybrid methods are increasingly used; they introduce machine learning (ML) as the deterministic part, along with kriging of the ML residuals as the stochastic part [8,9,10,11,12]

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