A comparison of machine learning algorithms for mapping soil iron parameters indicative of pedogenic processes by hyperspectral imaging of intact soil profiles

Xu, Shixiao; Zhao, Yanni; Wang, Meiyan; Shi, Xuezheng

doi:10.1111/ejss.13204

Cited by 17 publications

(14 citation statements)

References 57 publications

(93 reference statements)

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“…Hyperparameter tuning depends on several factors: sample size, classifier/regression models used,and model type. (Audebert, Le Saux, Lefèvre, & magazine, 2019;S. Xu, Zhao, Wang, & Shi, 2022;Yang & Shami, 2020) It's an additional step to improve the accuracy and performance of the model(Pannakkong, Thiwa-Anont, Singthong, Parthanadee, & Buddhakulsomsiri, 2022).…”

Section: Hyperparameter Optimizationmentioning

confidence: 99%

Machine Learning Application in G.I.S. and Remote Sensing: An Overview

Upreti¹

2022

Preprint

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Machine learning (ML) is a subdivision of artificial intelligence in which the machine learns from machine-readable data and information. It uses data, learns the pattern and predicts the new outcomes. Its popularity is growing because it helps to understand the trend and provides a solution that can be either a model or a product. Applications of ML algorithms have increased drastically in G.I.S. and remote sensing in recent years. It has a broad range of applications, from developing energy-based models to assessing soil liquefaction to creating a relation between air quality and mortality. Here, in this paper, we discuss the most popular supervised ML models (classification and regression) in G.I.S. and remote sensing. The motivation for writing this paper is that ML models produce higher accuracy than traditional parametric classifiers, especially for complex data with many predictor variables. This paper provides a general overview of some popular supervised non-parametric ML models that can be used in most of the G.I.S. and remote sensing-based projects. We discuss classification (Naïve Bayes (NB), Support Vector Machine (SVM), Random Forest (RF), Decision Trees (DT)) and regression models (Random Forest (RF), Support Vector Machine (SVM), Linear and Non-Linear) here. Therefore, the article can be a guide to those interested in using ML models in their G.I.S. and remote sensing-based projects

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Section: Hyperparameter Optimizationmentioning

confidence: 99%

Machine Learning Application in G.I.S. and Remote Sensing: An Overview

Upreti¹

2022

Preprint

View full text Add to dashboard Cite

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“…Hyperparameter tuning depends on several factors: sample size, classifier/regression models used, and model type [55][56][57] . It's an additional step to improve the accuracy and performance of the model [58] . For example, selection of the best polynomial features in linear regression models, number of trees in a random forest, number of layers and neurons in a neural network, maximum depth in decision trees, and learning rate for gradient descent [58] .…”

Section: Hyper Parameter Optimizationmentioning

confidence: 99%

“…It's an additional step to improve the accuracy and performance of the model [58] . For example, selection of the best polynomial features in linear regression models, number of trees in a random forest, number of layers and neurons in a neural network, maximum depth in decision trees, and learning rate for gradient descent [58] . Some common hyper parameter tuning techniques are grid search, randomized search, Bayesian optimization, sequential model-based optimization, and genetic algorithms [55] .…”

Section: Hyper Parameter Optimizationmentioning

confidence: 99%

Machine learning application in GIS and remote sensing: An overview

Upreti¹

2022

IJMRGE

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Machine learning (ML) is a subdivision of artificial intelligence in which the machine learns from machine-readable data and information. It uses data, learns the pattern and predicts the new outcomes. Its popularity is growing because it helps to understand the trend and provides a solution that can be either a model or a product. Applications of ML algorithms have increased drastically in G.I.S. and remote sensing in recent years. It has a broad range of applications, from developing energy-based models to assessing soil liquefaction to creating a relation between air quality and mortality. Here, in this paper, we discuss the most popular supervised ML models (classification and regression) in G.I.S. and remote sensing. The motivation for writing this paper is that ML models produce higher accuracy than traditional parametric classifiers, especially for complex data with many predictor variables. This paper provides a general overview of some popular supervised non-parametric ML models that can be used in most of the G.I.S. and remote sensing based projects. We discuss classification (Naïve Bayes (NB), Support Vector Machine (SVM), Random Forest (RF), Decision Trees (DT)) and regression models (Random Forest (RF), Support Vector Machine (SVM), Linear and Non-Linear) here. Therefore, the article can be a guide to those interested in using ML models in their G.I.S. and remote sensing based projects.

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“…Vibrational absorption characteristics occur in the SWIR regions between the wavelengths of 2080 and 2350 mm (Xu et al, 2022) (Figure 3b). These processes involve the bonds in a crystal lattice or molecular compound.…”

Section: Introductionmentioning

confidence: 99%

Monitoring sedimentary areas from mine waste products with Sentinel‐2 satellite images: A case study in theSEof Spain

Pereira

Alcalde-Aparicio

Ferrer-Juliá

et al. 2023

European J Soil Science

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Torrential rainfall regimes, among others, are the origin of accelerated soil erosion. The Spanish southeastern Mediterranean region is characterised by an arid climate regime affected by extreme erosion episodes with an important loss of sediments. Soil erosion effects are even more noticeable in areas where soil has been anthropically degraded, as in the mining district of Sierra Minera de Cartagena. The present research focuses on monitoring and mapping the changes in the sediment distribution of iron oxides and hydroxyl (OH−) bearing minerals caused by a cold drop known in Spanish as 'DANA' in September 2019 on the Rambla del Beal. This short rambla is fed by sediments from its drainage basin and by mining residues, irrigating a wide agricultural area. When discharges overflow the rambla channel, residues spread over its floodplain and reach the ecological protected coastal lagoon Mar Menor. The objective of the study was mapping the mineral distribution of the mining materials eroded from the source areas and sedimented in Rambla del Beal during a DANA. The study was carried out using a pre‐ and a post‐DANA image from the Sentinel‐2 satellite. After masking vegetation, urban areas and water bodies, different band ratios (B4/B3, B11/B12, B8A/B6) and a Spectral Angle Mapper (SAM) classification were applied. Sediment deposits were identified in wider areas after the DANA. Iron oxides increased their extension by 11.08% in the central area (B3/B4 with R2 of 0.84) and hydroxyl‐bearing minerals increased by 8.95% in the Rambla del Beal's headwaters (B11/B12 with R2 of 0.71). The SAM classification (with a 0.1 rad threshold and an overall accuracy of 87.33%) allowed the differentiation and classification of two ferric iron oxides (haematite and goethite) and one iron hydrous sulphate mineral (jarosite). Additionally, band ratio images were spatially overlaid with the soil land uses map layer of the cadastre in order to plot the land uses most affected by the transported sediments during the DANA. These results highlighted agricultural land as the areas (land uses) most affected by iron oxides deposition, as oxidation processes occur more rapidly in these areas. However, grassland and scrubland were the areas with the highest content of hydroxyl‐bearing minerals, as water is accumulated in these places, which favours hydrolysis reactions. Highlights Torrential events can relocate large volumes of contaminated sediments from former mining areas. Sentinel‐2 allows monitoring mine waste iron‐sulphate changes caused by weathering alteration. Spatial mineralogical pattern in sediments is controlled by geomorphology and flood dynamics. The proposed digital image analysis allows mapping short‐term evolution of mine waste sediments.

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A comparison of machine learning algorithms for mapping soil iron parameters indicative of pedogenic processes by hyperspectral imaging of intact soil profiles

Cited by 17 publications

References 57 publications

Machine Learning Application in G.I.S. and Remote Sensing: An Overview

Machine Learning Application in G.I.S. and Remote Sensing: An Overview

Machine learning application in GIS and remote sensing: An overview

Monitoring sedimentary areas from mine waste products with Sentinel‐2 satellite images: A case study in theSEof Spain

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