Use of Machine Learning Techniques in Soil Classification

Abstract

In the design of reliable structures, the soil classification process is the first step, which involves costly and time-consuming work including laboratory tests. Machine learning (ML), which has wide use in many scientific fields, can be utilized for facilitating soil classification. This study aims to provide a concrete example of the use of ML for soil classification. The dataset of the study comprises 805 soil samples based on the soil drillings of the new Gayrettepe–Istanbul Airport metro line construction. The dataset has both missing data and class imbalance. In the data preprocessing stage, first, data imputation techniques were applied to deal with the missing data. Two different imputation techniques were tested, and finally, the data were imputed with the KNN imputer. Later, a balance was achieved with the synthetic minority oversampling technique (SMOTE). After the preprocessing, a series of ML algorithms were tested with 10-fold cross-validation. Unlike the studies conducted in previous research, new gradient-boosting methods such as XGBoost, LightGBM, and CatBoost were tested, high classification accuracy rates of up to +90% were observed, and a significant improvement in the accuracy of prediction (when compared with previous research) was achieved.

A super-learner machine learning model for a global prediction of compression index in clays

“…This is a technique widely used in ML algorithms for dealing with missing values and it has been used in geotechnical issues satisfactorily (e.g. Aydın et al 2023;Díaz et al 2023). For the imputation of values, a multivariate feature imputation algorithm has been chosen (Little and Rubin 2019;Van Buuren and Oudshoorn 2000).…”

HybridTransferNet: soil image classification through comprehensive evaluation for crop suggestion

“…Soil classification predicated on physical and chemical properties has been addressed using a spectrum of machine learning algorithms, including convolutional neural nets (CNN), naive bayes, and decision trees [5]- [7]. Furthermore, the application of machine learning in agriculture extends beyond soil classification, encompassing the prediction of crop yields, the detection of diseases and weeds, species identification, livestock management, and the implementation of intelligent irrigation and harvesting systems [8]- [10].…”

“…In the context of soil image classification, prior works have explored different facets and methodologies. One study conducted a comparative analysis of diverse techniques for selecting wrapper features in conjunction with classification techniques to recommend the most suitable crops for specific land conditions [7]. A comprehensive review underscored the potential advantages of leveraging machine learning for estimating agricultural productivity, identifying weeds and diseases, predicting soil parameters, and managing livestock [8].…”

Machine Learning Techniques for Soil Characterization Using Cone Penetration Test Data

“…Many ML algorithms, such as gradient boosting, random forest, support vector machine (SVM) artificial neural network (ANN), and decision trees (DT), have been used in various geotechnical applications, including soil classification [27][28][29][30][31][32][33], V s prediction [23][24][25][26]34], liquefaction analysis [35][36][37][38][39][40], stability analysis [41][42][43][44][45], and settlement prediction [46][47][48]. The application of ML algorithms in geotechnical engineering has shown promising results in terms of efficiency and accuracy.…”