Potential of Biochar as Soil Amendment: Prediction of Elemental Ratios from Pyrolysis of Agriculture Biomass Using Artificial Neural Network

Liew, Yi Wei; Arumugasamy, Senthil Kumar; Selvarajoo, Anurita

doi:10.1007/s11270-022-05510-2

Cited by 18 publications

(7 citation statements)

References 30 publications

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“…Levenberg-Marquardt is a network training function that updates the weight and bias values according to the optimization. This algorithm is highly recommended, although it requires more memory than other algorithms [ 59 – 61 ].…”

Section: Methodsmentioning

confidence: 99%

Accuracy Assessment of Kriging, artificial neural network, and a hybrid approach integrating spatial and terrain data in estimating and mapping of soil organic carbon

Kılıç

Gündoğan²,

Günal³

et al. 2022

PLoS ONE

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This study aimed to produce a soil organic carbon (SOC) content map with high accuracy and spatial resolution using the most effective factors in the model. The spatial SOC estimation success of Inverse Distance Weighting (IDW), Ordinary Kriging (OK), Empirical Bayesian Kriging (EBK), Multi-Layered Perception Network (MLP) and MLP-OK Hybrid models were compared to obtain the most reliable model in estimating the SOC content. The study area was located in Besni district in the Southeastern Anatolia Region of Turkey. Total of 132 surface (0–30 cm) soil samples were collected from the covers 1330 km2 land and analyzed for SOC, lime, clay and sand content and soil reaction included in the estimation models. Mean annual precipitation and temperature, elevation, compound topographic index, enhanced vegetation and normalized difference vegetation index, were also used as the inputs in the modelling. The spatial distribution of SOC was determined using a MLP and a two-stage ensemble model (MLP-OK) combining the estimation of OK residuals. Soil surveys and covariates were used to train and validate the MLP-OK hybrid model. The MLP-OK model provided a more accurate estimation of SOC content with minimal estimation errors (ME: -0.028, 45 MAE: 0.042, RMSE: 0.066) for validation points compared to the other models. The MLP-OK model outperformed other models by 75.09 to 77.92%. The MLP-OK model estimated the lower and upper limits of the estimated and the measured values in a consistent manner compared to the other models. The spatial distribution map of SOC content obtained by ANN-kriging approach was significantly affected by ancillary variables, and revealed more detail than other interpolation methods in the northern, central, southwestern and southeastern parts of the study area. The results revealed that the assembling of MLP with OK model can contribute to obtain more reliable regional, national and global spatial soil information.

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Section: Methodsmentioning

confidence: 99%

Accuracy Assessment of Kriging, artificial neural network, and a hybrid approach integrating spatial and terrain data in estimating and mapping of soil organic carbon

Kılıç

Gündoğan²,

Günal³

et al. 2022

PLoS ONE

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show abstract

“…The H/C and O/C ratios in produced biochar determine its stability, aromaticity, and polarity. The decrease in H/C and O/C ratios is in accordance with the high aromaticity and low polarity of biochar, suggesting that the biochar has excellent resistance to microbial decomposition, making it a strong contender in the MFC industry (Liew et al 2022). The N content of biochar is a critical factor for its fertilizer application.…”

Section: Crop Residuesmentioning

confidence: 80%

Review of biochar production via crop residue pyrolysis: Development and perspectives

Gupta

Zhang

et al. 2023

Bioresource Technology

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“…The MLP network architecture was constructed in the Matlab R2021a with one input, two hidden and one output layers. In addition, the network was trained by optimizing the weight and bias values using the Levenberg-Marquardt training algorithm and a feed-forward backpropagation network (Samui and Sitharam 2011; Liew et al 2022). In determining the number of hidden neurons, the number of neurons was determined considering the Root Mean Square Error value starting from 1 neuron (Sergeev et al…”

Section: Multilayer Perceptron Network Spatial Modeling Approachmentioning

confidence: 99%

Improvement of spatial estimation for soil organic carbon stocks in Yuksekova plain using Sentinel 2 imagery and gradient descent–boosted regression tree

Budak

Günal²,

Kılıç

et al. 2023

Environ Sci Pollut Res

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Carbon sequestration in earth surface is higher than the atmosphere, and the amount of carbon stored in wetlands is much greater than all other land surfaces. The purpose of this study was to estimate soil organic carbon stocks (SOCS) and investigate spatial distribution pattern of Yuksekova wetlands and surrounding lands in Hakkari province of Turkey using machine learning and remote sensing data. Total carbon stock in study area was calculated at 10-cm vertical resolution in 0 to 30 cm depth for 50 sampling locations. Vegetation, soil and moisture indices were calculated using Sentinel 2 Multispectral Sensor Instrument (MSI) data. Signi cant correlations were obtained between the indices and SOCS, thus, the remote sensing indices were used as covariates in Multi-Layer Perceptron Neural Network (MLP) and Gradient Descent Boosted Regression Tree (GBDT) machine learning models. Mean Absolute Error, Root Mean Square Error and Mean Absolute Percentage Error were 3.94 (Mg C ha − 1 ), 6.64 (Mg C ha − 1 ) and 9.97%, respectively. The Simple Ratio Clay Index (SRCI), which represents the soil texture, was the most important factor in the SOCS estimation variance. In addition, the relationship between SRCI and Topsoil Grain Size Index revealed that topsoil clay content is a highly important parameter in spatial variation of SOCS. The spatial SOCS values obtained using the GBDT model and the mean SOCS values of the CORINE land lover classes were signi cantly different. The wetlands had the highest SOCS (61.46 Mg C ha − 1 ), followed by the lands principally occupied by natural vegetation and used as rangelands around the wetland (50.22 Mg C ha − 1 ). Environmental conditions have signi cant effect on SOCS which has high spatial variation in the study area. Reliable spatial SOCS information was obtained with the combination of Sentinel-2 guided multi-index remote sensing modeling strategy and the GBDT model. Therefore, the spatial estimation of SOCS can be successfully carried out with up-to-date machine learning algorithms only using remote sensing data. Reliable estimation of SOCS in wetlands and surrounding lands can help understand policy and decision makers the importance of wetlands in mitigating the negative impacts of global warming .

show abstract

Potential of Biochar as Soil Amendment: Prediction of Elemental Ratios from Pyrolysis of Agriculture Biomass Using Artificial Neural Network

Cited by 18 publications

References 30 publications

Accuracy Assessment of Kriging, artificial neural network, and a hybrid approach integrating spatial and terrain data in estimating and mapping of soil organic carbon

Accuracy Assessment of Kriging, artificial neural network, and a hybrid approach integrating spatial and terrain data in estimating and mapping of soil organic carbon

Review of biochar production via crop residue pyrolysis: Development and perspectives

Improvement of spatial estimation for soil organic carbon stocks in Yuksekova plain using Sentinel 2 imagery and gradient descent–boosted regression tree

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