A soft-computing approach to estimate soil electrical conductivity

Motie, Jalal Baradaran; Aghkhani, Mohammad Hossein; Rohani, Abbas; Lakzian, Amir

doi:10.1016/j.biosystemseng.2021.02.015

Cited by 16 publications

(6 citation statements)

References 35 publications

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“…Mamdani and Larsen showed the worst results considering all the accuracy measures (> 5.2, > 0.3, and > 32 for MAE, MAPE, and MSE). Similar results were reported by other researchers when comparing RSM and other methods 40 …”

Section: Resultssupporting

confidence: 91%

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Effects of an alternative bleaching method for yerba mate leaves on total phenolic content: evaluation and prediction with soft‐computing approaches

et al. 2022

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BACKGROUND The effects of an alternative bleaching method on the total phenolic content (TPC) and antioxidant activity (AA) of yerba mate extracts were evaluated. Traditional bleaching (‘zapeco’) was compared with submerging the leaves in water followed by a hot air oven‐drying process. Seven different approaches, i.e. linear model (LM), response surface model (RSM), Mamdani, Larsen, adaptive neuro‐fuzzy inference system (ANFIS) with the product (Prod) and the minimum (Min) operators, and ANFIS with automatically membership functions (Auto), were employed to compare the TPC of yerba mate extracts based on drying temperature and AA assays. RESULTS The results showed that if leaves were bleached followed by drying at higher temperatures, we obtained higher AA and TPC values. For submerging bleaching treatment, RSM model delivered the best accuracy measures with a mean absolute error (MAE), average absolute percentage error (MAPE), and mean squared error (MSE) of 0.128, 0.006, and 0.028, respectively. The ANFIS Auto model was the best for traditional bleaching treatment, with MAE, MAPE, and MSE of 0.490, 0.013, and 0.612, respectively. CONCLUSION The results suggest a second‐order linear relation between drying temperature, AA assays to TPC, and a high level of relation complexity of drying temperature, AA assays, and TPC. The evaluated soft‐computing approaches have the excellent ability to estimate TPC from bleached leaves. © 2022 Society of Chemical Industry.

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Section: Resultssupporting

confidence: 91%

“…Similar results were reported by other researchers when comparing RSM and other methods. 40 Since RSM is the most feasible in predicting for the boiling treatment, the second-order contribution of each input variable (i.e. drying temperature, ABTS, DPPH, and FRAP assays) to the output variable (i.e.…”

Section: Soft-computing Approachesmentioning

confidence: 99%

Effects of an alternative bleaching method for yerba mate leaves on total phenolic content: evaluation and prediction with soft‐computing approaches

et al. 2022

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“…It is one of the approaches used to overcome the complexities of a large number of regulatory variables, unknown mechanisms, and computational difficulties. RSM is successfully used by researchers in soil sciences to overcome its complexities (Motie et al, 2021). The equation below characterizes the association between the response and predictor variables.…”

Section: Methodsmentioning

confidence: 99%

Evaluation of the impact of wastewater irrigation on the soil's dielectric parameters

Palta,

Kumar,

Kaur

2024

Land Degrad Dev

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This study investigates the impact of wastewater irrigation on soil dielectric parameters, focusing on the often‐overlooked aspect of dielectric properties in contaminated soil. Wastewater, a significant contributor to soil and groundwater degradation, has long‐term effects on soil physical, chemical, and dielectric characteristics. Experimental measurements conducted overtime at 2.45 and 5.8 GHz frequencies reveal a correlation between wastewater irrigation and changes in dielectric properties. Emphasizing time variation as a pivotal independent variable, the study sheds light on its substantial influence on soil dielectric behavior. Notably, the research introduces an alternative methodology for the comprehensive evaluation of wastewater‐induced pollution effects on soil properties. This approach is substantiated by meticulous physicochemical analyses conducted on both pollution sources and the affected soil. The study further presents a robust response surface method‐based model, showcasing impressive accuracy with dielectric properties (ε′ and ε″) and penetration depth with R2 values of 0.9845, 0.9831, and 0.9528, respectively. Beyond unveiling crucial insights into the intricate interplay of wastewater and soil dielectric behavior, this research contributes to the development of effective strategies for pollution monitoring and management in soil ecosystems.

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“…In another study by Hara et al [61], a comparison of ANN and multi linear regression models showed that the ANN predicted the pea (Pisum sativum L.) seed yield more accurately than the regression model. The comparison of ANN and RSM models in estimating parameters other than seed yield also showed that ANN models were more effective than RSM models in predicting target parameters [62][63][64].…”

Section: Seed Prediction By Rsm and Annmentioning

confidence: 96%

RSM and ANN Modeling of Camelina (Camelina sativa L. Crantz) Seed Yield as Affected by Nitrogen, Sulfur, Cow Manure, and Row Spacing

Yari,

Rokhzadi,

Shamsi

et al. 2024

Horticulturae

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Camelina [Camelina sativa (L.) Crantz] is an annual versatile oilseed crop of the Brassicaceae family, with an increasingly cultivated area. Predicting camelina seed yield response to fertilization and planting density is of great importance in understanding production potential and management planning. Therefore, the current study aimed to estimate the seed yield of camelina by response surface methodology (RSM) and artificial neural network (ANN) as affected by different levels of planting row spacing and nitrogen (N), sulfur (S), and cow manure (CM) fertilization. The experiment was conducted in two growing years of 2019–2020 and 2020–2021, based on a central composite design with four factors including row spacing (15–35 cm), N (0–200 kg ha−1), S (0–100 kg ha−1), and CM (0–40 t ha−1). The RSM models for seed yield versus fertilization and row spacing factors in both years were statistically significant and had an acceptable predictive ability. Camelina seed yield decreased with increasing row spacing but showed a positive response to increasing the amount of N, S, and CM fertilizers. Comparing the performance of the models showed that, although the RSM models were significant and had the necessary efficiency in predicting camelina seed yield, the ANN models were more accurate. The performance criteria of coefficient of determination (R2), root mean square error (RMSE), standard error of prediction (SEP), mean absolute error (MAE), and Akaike information criterion (AICc) averaged over the two years for the RSM model were 0.924, 51.60, 5.51, 41.14, and 394.05, respectively, and for the ANN model were 0.968, 32.62, 3.54, 19.55, and 351.33, respectively. Based on the results, the ANN modeling can be used in predicting camelina seed yield in field conditions with more confidence than the RSM technique.

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A soft-computing approach to estimate soil electrical conductivity

Cited by 16 publications

References 35 publications

Effects of an alternative bleaching method for yerba mate leaves on total phenolic content: evaluation and prediction with soft‐computing approaches

Effects of an alternative bleaching method for yerba mate leaves on total phenolic content: evaluation and prediction with soft‐computing approaches

Evaluation of the impact of wastewater irrigation on the soil's dielectric parameters

RSM and ANN Modeling of Camelina (Camelina sativa L. Crantz) Seed Yield as Affected by Nitrogen, Sulfur, Cow Manure, and Row Spacing

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