An effective implementation and assessment of a random forest classifier as a soil spatial predictive model

Shukla, Gaurav; Garg, Rahul; Srivastava, H. S.; Garg, Pradeep

doi:10.1080/01431161.2018.1430399

Cited by 29 publications

(9 citation statements)

References 58 publications

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“…The RF for regression is extensively used in DSM (e.g., [28][29][30][31][32][33]) with very positive results in the prediction of different soil parameters. More importantly though, it works equally well with skewed and normally distributed variables, without the need of statistical assumptions or restrictions that other methods demand.…”

Section: Random Forests (Rf) and Quantile Regression Forests (Qrf)mentioning

confidence: 99%

Prediction and Uncertainty Capabilities of Quantile Regression Forests in Estimating Spatial Distribution of Soil Organic Matter

Nikou

Tziachris²

2022

IJGI

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One of the core tasks in digital soil mapping (DSM) studies is the estimation of the spatial distribution of different soil variables. In addition, however, assessing the uncertainty of these estimations is equally important, something that a lot of current DSM studies lack. Machine learning (ML) methods are increasingly used in this scientific field, the majority of which do not have intrinsic uncertainty estimation capabilities. A solution to this is the use of specific ML methods that provide advanced prediction capabilities, along with innate uncertainty estimation metrics, like Quantile Regression Forests (QRF). In the current paper, the prediction and the uncertainty capabilities of QRF, Random Forests (RF) and geostatistical methods were assessed. It was confirmed that QRF exhibited outstanding results at predicting soil organic matter (OM) in the study area. In particular, R2 was much higher than the geostatistical methods, signifying that more variation is explained by the specific model. Moreover, its uncertainty capabilities as presented in the uncertainty maps, shows that it can also provide a good estimation of the uncertainty with distinct representation of the local variation in specific parts of the area, something that is considered a significant advantage, especially for decision support purposes.

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Section: Random Forests (Rf) and Quantile Regression Forests (Qrf)mentioning

confidence: 99%

Prediction and Uncertainty Capabilities of Quantile Regression Forests in Estimating Spatial Distribution of Soil Organic Matter

Nikou

Tziachris²

2022

IJGI

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“…For example, [60] reviews the usefulness of high-resolution LiDAR sensor and its application for urban land cover classification, or in [7], an algorithmic perspective review for processing of hyperspectral images is provided. Over the past few years, deep learning algorithms have drawn attention for several RS applications [33], [61], and as such, several review articles have been published on this topic. For instance, three typical models of deep learning algorithms, namely deep belief network, convolutional neural networks, and stacked auto-encoder, were analyzed in [62].…”

Section: Introduction Ecent Advances In Remote Sensing (Rs) Technomentioning

confidence: 99%

“…For example, CNN has shown performance improvements over SVM and RF [69]- [71]. However, one of the main problems with deep learning approaches is their hidden layers; "black box" [61] nature, which results in the loss of interpretability (see Fig. 1).…”

Section: Introduction Ecent Advances In Remote Sensing (Rs) Technomentioning

confidence: 99%

Support Vector Machine Versus Random Forest for Remote Sensing Image Classification: A Meta-Analysis and Systematic Review

Sheykhmousa¹,

Mahdianpari

Ghanbari

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

651

195

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Several machine-learning algorithms have been proposed for remote sensing image classification during the past two decades. Among these machine learning algorithms, Random Forest (RF) and Support Vector Machines (SVM) have drawn attention to image classification in several remote sensing applications. This paper reviews RF and SVM concepts relevant to remote sensing image classification and applies a meta-analysis of 251 peer-reviewed journal papers. A database with more than 40 quantitative and qualitative fields was constructed from these reviewed papers. The meta-analysis mainly focuses on: (1) the analysis regarding the general characteristics of the studies, such as geographical distribution, frequency of the papers considering time, journals, application domains, and remote sensing software packages used in the case studies, and (2) a comparative analysis regarding the performances of RF and SVM classification against various parameters, such as data type, RS applications, spatial resolution, and the number of extracted features in the feature engineering step. The challenges, recommendations, and potential directions for future research are also discussed in detail. Moreover, a summary of the results is provided to aid researchers to customize their efforts in order to achieve the most accurate results based on their thematic applications.

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“…45,46 Deep learning algorithms such as convolutional neural networks (CNN) have shown remarkable achievements on classification data as compared to SVM and RF, [47][48][49] due to their ability to retrieve complex patterns and useful features from remote sensing images. Although deep learning approaches have some of the limitations such as their hidden layers, black box 50 nature that results in low interpretability. Deep learning approaches are also highly dependent on the amount of training data as compared to SVM and RF.…”

Section: Introductionmentioning

confidence: 99%

Support vector machine versus convolutional neural network for hyperspectral image classification: A systematic review

Kaul

2022

Concurrency and Computation

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Various machine learning and deep learning techniques have been proposed for classification purposes in the case of hyperspectral imaging. Among all the machine learning techniques support vector machine (SVM) has been a promising classification algorithm in the case of remote sensing applications, particularly in the field of hyperspectral imaging. In the case of deep learning techniques, the convolutional neural network is gaining much attention from researchers for classification purposes and has given reliable results in this field, thus is being used widely by researchers. So, in this article, a systematic review about the two most used hyperspectral classification techniques that is, SVM and CNN is given. A total of 86 papers from the four well-known journals belonging to the field of remote sensing have been reviewed where the methodologies used by different authors, datasets used, results acquired, contribution and shortcomings have been put forth. This meta-analysis generally focuses on the recent SVM and CNN methodologies that are proposed by various authors. A summary of the best results obtained from SVM and CNN in terms of classification accuracies is also provided to help the researchers working in this area achieve better results.

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An effective implementation and assessment of a random forest classifier as a soil spatial predictive model

Cited by 29 publications

References 58 publications

Prediction and Uncertainty Capabilities of Quantile Regression Forests in Estimating Spatial Distribution of Soil Organic Matter

Prediction and Uncertainty Capabilities of Quantile Regression Forests in Estimating Spatial Distribution of Soil Organic Matter

Support Vector Machine Versus Random Forest for Remote Sensing Image Classification: A Meta-Analysis and Systematic Review

Support vector machine versus convolutional neural network for hyperspectral image classification: A systematic review

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