Improving prediction accuracy for acid sulfate soil mapping by means of variable selection

Abstract

Acid sulfate soils can cause environmental damage and geotechnical problems when drained or exposed to oxidizing conditions. This makes them one of the most harmful soils found in nature. In order to reduce possible damage derived from this type of soil, it is fundamental to create occurrence maps showing their localization. Nowadays, occurrence maps can be created using machine learning techniques. The accuracy of these maps depends on two factors: the dataset and the machine learning method. Previously, different machine learning methods were evaluated for acid sulfate soil mapping. To improve the precision of the acid sulfate soil probability maps, in this qualitative modeling study we have added more environmental covariates (17 in total). Since a greater number of covariates does not necessarily imply an improvement in the prediction, we have selected the most relevant environmental covariates for the classification and prediction of acid sulfate soils. For this, we have applied eleven different variable selection methods. The predictive abilities of each group of selected variables have been analyzed using Random Forest and Gradient Boosting. We show that the selection of each environmental covariate as well as the relationship between them are extremely important for an accurate prediction of acid sulfate soils. Among the variable selection methods analyzed, Random Forest stands out, as it is the one that has best selected the relevant covariates for the classification of these soils. Furthermore, the combination of two variable selection methods can improve the prediction of the model. Contrary to the general belief, a low correlation between the covariates does not guarantee a good performance of the model. In general, Random Forest has given better results in the prediction than Gradient Boosting. From the best results obtained, an acid sulfate soils occurrence map has been created. Compared with previous studies in the same area, variable selection has improved the accuracy by 15%–17% for the models based on Random Forest. The present study confirms the importance of variable selection for the prediction of acid sulfate soils.

Yield gap reduction of pineapple (Ananas comosus L.) by site-specific nutrient management

“…This should be due to the great presence of acids in the soil, which was shown via the total acidity result at both sites. This was the result of the oxidation of acid-sulfate-bearing materials in soil [ 10 , 11 ]. Low soil pH adversely affects plant development, because the condition can not only elevate concentrations of soil toxicities, e.g., Fe 2+ , Al 3+ , and Mn 2+ [ 39 ], but also minimize the nutrient availability of P, Mg, and Ca [ 40 ], leading to a lack of essential nutrients for plants.…”

“…Meanwhile, pineapple is one of the most popular crops that are grown in ASS in Vietnam [ 9 ], because of its fairly adaptability to the conditions of ASS [ 8 ]. However, ASS can cause damage to agriculture when oxidized [ 10 ]. The oxidation of ASS can decrease soil pH below 4.0 [ 11 ], which is undesirable for the growth of crops.…”