Training algorithms for artificial neural network in predicting of the content of chemical elements in the upper soil layer

Shichkin, Andrey; Buevich, Alexander; Sergeev, Alexander; Baglaeva, Elena; Subbotina, Irina; Vasilev, Julian; Kehayova-Stoycheva, Maria

doi:10.1063/1.5082119

Cited by 11 publications

(9 citation statements)

References 12 publications

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“…The method has similar data processes as a biological neural with nonlinear mapping structures, which consists of a set of interconnected units (neurons) [117]. The input neurons are predictors, linking to one layer of hidden neurons and finally linking to the output variables [118]. To obtain accurate prediction results, the network model is trained first by a set of observations.…”

Section: New Emerging Methods To Predict Soil Propertiesmentioning

confidence: 99%

“…ANNs outperform traditional statistics in handling large datasets even when the input data are noisy with low levels of precision due to the ability to reduce bias by evenly distributing training data across classes [119]. Various researchers have employed ANNs for efficient prediction of quantitative soil chemical and hydrological properties [118,120,121] and adequate mapping categorical soil taxonomic classes [122][123][124][125][126][127][128][129] based on DTMs and environmental variables. Zhao et al [93] also tested the feasibility of using ANNs for soil drainage classification and found an accuracy of 52% between field observations and digital classification.…”

Section: New Emerging Methods To Predict Soil Propertiesmentioning

confidence: 99%

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Application of Topographic Analyses for Mapping Spatial Patterns of Soil Properties

Li¹,

McCarty²

2019

Earth Observation and Geospatial Analyses [Working Title]

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Landscape topography is a key parameter impacting soil properties on the earth surface. Strong topographic controls on soil morphological, chemical, and physical properties have been reported. This chapter addressed applications of topographical information for mapping spatial patterns of soil properties in recent years. Objectives of this chapter are to provide an overview of (1) impacts of topographic heterogeneity on the spatial variability in soil properties and (2) commonly used topography-based models in soil science. A case study was provided to demonstrate the feasibility of applying topography-based models developed in field sites to predict soil property over a watershed scale. A large-scale soil property map can be obtained based on topographic information derived from high-resolution remotely sensed data, which would benefit studies in areas with limited data accesses or needed to extrapolate findings from representative sites to larger regions.

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Section: New Emerging Methods To Predict Soil Propertiesmentioning

confidence: 99%

Section: New Emerging Methods To Predict Soil Propertiesmentioning

confidence: 99%

Application of Topographic Analyses for Mapping Spatial Patterns of Soil Properties

Li¹,

McCarty²

2019

Earth Observation and Geospatial Analyses [Working Title]

View full text Add to dashboard Cite

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“…Adequate training, validation, testing, and overall prediction accuracy were observed when the LM algorithm was used (Table 7 ). The LM algorithm which may be the fastest of the three training algorithms specifically works with loss functions presented in the form of a sum of squared errors (SSE) 45 , 46 . Unfortunately, LM cannot be applied to the cross-entropy error and the root mean squared error functions 46 .…”

Section: Resultsmentioning

confidence: 99%

“…As a result, the LM is the recommended choice with better performance in terms of rapidity and the overfitting problem when there are a few thousand instances and a few hundred parameters for training the ANN 46 , 47 . In an unrelated study, the LM training algorithm was found to show the highest accuracy in comparison to different training algorithms in a MLP model that forecasted chemical elements distribution in the topsoil 45 .…”

Section: Resultsmentioning

confidence: 99%

A modeling method for the development of a bioprocess to optimally produce umqombothi (a South African traditional beer)

Hlangwani

Doorsamy

Adebiyi

et al. 2021

Sci Rep

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Bioprocess development for umqombothi (a South African traditional beer) as with other traditional beer products can be complex. As a result, beverage bioprocess development is shifting towards new systematic protocols of experimentation. Traditional optimization methods such as response surface methodology (RSM) require further comparison with a relevant machine learning system. Artificial neural network (ANN) is an effective non-linear multivariate tool in bioprocessing, with enormous generalization, prediction, and validation capabilities. ANN bioprocess development and optimization of umqombothi were done using RSM and ANN. The optimum condition values were 1.1 h, 29.3 °C, and 25.9 h for cooking time, fermentation temperature, and fermentation time, respectively. RSM was an effective tool for the optimization of umqombothi’s bioprocessing parameters shown by the coefficient of determination (R2) closer to 1. RSM significant parameters: alcohol content, total soluble solids (TSS), and pH had R2 values of 0.94, 0.93, and 0.99 respectively while the constructed ANN significant parameters: alcohol content, TSS, and viscosity had R2 values of 0.96, 0.96, and 0.92 respectively. The correlation between experimental and predicted values suggested that both RSM and ANN were suitable bioprocess development and optimization tools.

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“…The weights can be adjusted iteratively on the basis of the training dataset. Including topographic information and other environmental variables, this method has been successfully applied to identify categorical characters, such as soil taxonomic classes and drainage classification [79,[86][87][88][89], and to predict quantitative variables including soil chemical and hydrological properties [90][91][92].…”

Section: Advanced Statistical Methodsmentioning

confidence: 99%

Use of Topographic Models for Mapping Soil Properties and Processes

Li¹,

McCarty²,

Du³

et al. 2020

Soil Systems

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Landscape topography is an important driver of landscape distributions of soil properties and processes due to its impacts on gravity-driven overland and intrasoil lateral transport of water and nutrients. Rapid advancements in aerial, space, and geographic technologies have led to large scale availability of digital elevation models (DEMs), which have proven beneficial in a wide range of applications by providing detailed topographic information. In this report, we presented a summary of recent topography-based soil studies and reviewed five main groups of topographic models in geospatial analyses widely used for soil sciences. We then compared performances of two types of topography-based models—topographic principal component regression (TPCR) and TPCR-kriging (TPCR-Kr)—to ordinary kriging (OKr) models in mapping spatial patterns of soil organic carbon (SOC) density and redistribution (SR) rate. The TPCR and OKr models were calibrated at an agricultural field site that has been intensively sampled, and the TPCR and TPCR-Kr models were evaluated at another field of interest with two sampling transects. High-resolution topographic variables generated from light detection and ranging (LiDAR)-derived DEMs were used as inputs for the TPCR model building. Both TPCR and OKr models provided satisfactory results on SOC density and SR rate estimations during model calibration. The TPCR models successfully extrapolated soil parameters outside of the area in which the model was developed but tended to underestimate the range of observations. The TPCR-Kr models increased the accuracies of estimations due to the inclusion of residual kriging calculated from observations of transects for local correction. The results suggest that even with low sample intensives, the TPCR-Kr models can reduce estimation variances and provide higher accuracy than the TPCR models. The case study demonstrated the feasibility of using a combination of linear regression and spatial correlation analysis to localize a topographic model and to improve the accuracy of soil property predictions in different regions.

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Training algorithms for artificial neural network in predicting of the content of chemical elements in the upper soil layer

Abstract: Multilayer perceptron, generalized regression neural network, and hybrid model in predicting the spatial distribution of impurity in the topsoil of urbanized area

Cited by 11 publications

References 12 publications

Application of Topographic Analyses for Mapping Spatial Patterns of Soil Properties

Application of Topographic Analyses for Mapping Spatial Patterns of Soil Properties

A modeling method for the development of a bioprocess to optimally produce umqombothi (a South African traditional beer)

Use of Topographic Models for Mapping Soil Properties and Processes

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