Investigating the capability of estimating soil thermal conductivity using topographical attributes for the Southern Great Plains, USA

Bayat, Hossein; Ebrahimzadeh, Golnaz; Mohanty, Binayak P.

doi:10.1016/j.still.2020.104811

Cited by 14 publications

(3 citation statements)

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“…They are important for devising appropriate irrigation models for different soils (Zhang et al., 2019), managing soil moisture in general (Sanuade et al., 2020), estimating soil surface evaporation, assessing organic matter decomposition (Khaledi et al., 2023), and enhancing crop growth (Abu‐Hamdeh, 2000; Abu‐Hamdeh & Reeder, 2000). They are increasingly important also for designing energy‐efficient building systems and geothermal piles (Akrouch et al., 2016; Bayat et al., 2021; Moradshahi et al., 2021). As climate change influences the frequency of forest fires (Flannigan et al., 2000), accurate models of soil heat transfer are critical to predict soil temperature variations, temperature‐induced changes in soil quality (e.g., organic matter content), and carbon transfer to the atmosphere (Caon et al., 2014; Conant et al., 2011; Qi et al., 2016).…”

Section: Introductionmentioning

confidence: 99%

Pore unit cell network modeling of the thermal conductivity dynamics in unsaturated sandy soils: Unveiling the role of spanning‐wetting phase cluster

Mirghafari,

Helforoosh,

Nikooee

et al. 2024

Vadose Zone Journal

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As the world struggles with climate change and energy crises, understanding the role of soil in the food–water–energy nexus becomes increasingly critical. Accurately estimating the soil thermal conductivity drying curve is essential for assessing the impacts of temperature on soil biota and crop growth, environmental changes due to forest fires and global warming, and for designing geo‐energy extraction techniques such as geothermal energy piles. Existing empirical models often fail to accurately estimate the soil thermal conductivity (TC), particularly in pendular soil moisture regimes where they do not capture sharp changes in TC. This study introduces a novel approach using a pore unit cell network model to more accurately describe the dynamics of TC in variably saturated soils. A quadratic parallel scheme within each soil pore unit cell links the TCs of solid, water, and air to the overall effective conductivity. By modeling air invasion in the pore network model and employing the proposed equation, we determined the unsaturated soil TC based on varying local conductivities. The model effectively captures the significant decrease in conductivity in the pendular saturation regime, associated with the shrinkage of the spanning‐wetting cluster. Quantitative analyses showed a substantial improvement in prediction accuracy compared to existing models, especially under varying moisture conditions. Our findings have significant implications for better characterizing soil thermal and hydraulic properties, which are crucial for resource management in a changing climate and advancing geo‐energy technologies.

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Section: Introductionmentioning

confidence: 99%

Pore unit cell network modeling of the thermal conductivity dynamics in unsaturated sandy soils: Unveiling the role of spanning‐wetting phase cluster

Mirghafari,

Helforoosh,

Nikooee

et al. 2024

Vadose Zone Journal

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“…ANN is broadly applied in various areas of environmental engineering such as air quality monitoring [20], soil quality monitoring [21], and water quality prediction [22]. The use of ANN was found to be superior in performance as it was implemented in various fields including medicine [23][24][25], geotechnical engineering [26], petroleum engineering [27], construction technology [28], building energy [29], and waste management [30], as compared to other models, such as linear and SVM models. Moreover, it was also found out that integrating an optimization technique to ANN further improved its performance as compared to linear and SVM models as observed in the studies of Sun et al in 2019 [31] and Zhang et al in 2020 [32].…”

Section: Introductionmentioning

confidence: 99%

Neuro-Particle Swarm Optimization Based In-Situ Prediction Model for Heavy Metals Concentration in Groundwater and Surface Water

Jesus

Senoro

Cruz

et al. 2022

Toxics

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Limited monitoring activities to assess data on heavy metal (HM) concentration contribute to worldwide concern for the environmental quality and the degree of toxicants in areas where there are elevated metals concentrations. Hence, this study used in-situ physicochemical parameters to the limited data on HM concentration in SW and GW. The site of the study was Marinduque Island Province in the Philippines, which experienced two mining disasters. Prediction model results showed that the SW models during the dry and wet seasons recorded a mean squared error (MSE) ranging from 6 × 10−7 to 0.070276. The GW models recorded a range from 5 × 10−8 to 0.045373, all of which were approaching the ideal MSE value of 0. Kling–Gupta efficiency values of developed models were all greater than 0.95. The developed neural network-particle swarm optimization (NN-PSO) models for SW and GW were compared to linear and support vector machine (SVM) models and previously published deterministic and artificial intelligence (AI) models. The findings indicated that the developed NN-PSO models are superior to the developed linear and SVM models, up to 1.60 and 1.40 times greater than the best model observed created by linear and SVM models for SW and GW, respectively. The developed models were also on par with previously published deterministic and AI-based models considering their prediction capability. Sensitivity analysis using Olden’s connection weights approach showed that pH influenced the concentration of HM significantly. Established on the research findings, it can be stated that the NN-PSO is an effective and practical approach in the prediction of HM concentration in water resources that contributes a solution to the limited HM concentration monitored data.

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“…(2020), yüzeye yakın toprağın hidrotermodinamiğine dayanarak, toprağın ısı akışının tahmininde zamana bağlı ısı iletkenliği denklemini kullanmış; aynı zamanda ısı akışının tahmininde toprağın ısı iletkenliğinin dikey değişiminin ve su buharı katkısının dikkate alındığını vurgulamışlardır. Toprağın ısı iletkenliğinin incelenmesi ve ısı taşınımı denkleminin güvenilir düzeyde toprak sıcaklığının tahmin edilmesinde kullanılması, toprağın ısısal özelliklerinin (ısı kapasitesi, ısısal yayınım, ısı iletkenliği katsayısı ve ısı akışı) detayı olarak belirlenmesini gerektirmektedir (Evett ve ark., 2012;Usowicz ve ark., 2013;Ekberli ve Gülser, 2014;Ekberli ve Sarılar, 2015;Arkhangelskaya ve Lukyashchenko, 2018;Gülser ve ark., 2018;Gülser ve Ekberli, 2019;Xie ve ark., 2019;Yadav ve ark., 2020;Bayat, 2021).…”

Section: Introductionunclassified

Toprağın zamana bağlı olmayan bir boyutlu ısı iletkenliğinin incelenmesi

Ekberli¹,

Gülser²,

Dengi̇z³

2021

Anadolu Journal of Agricultural Sciences

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Toprakların bazı ısısal özelliklerine bağlı olarak zamana bağlı olmayan sıcaklık alanının belirlenmesi, toprak verimliliğinin iyileştirilmesi ve korunmasında, bitki gelişimi için optimum koşulların oluşturulmasında gereklidir. Bu çalışmada İnceptisol ve Entisol toprakların bazı ısısal özelliklerini göz önüne alarak, zamana bağlı olmayan ısı iletkenliği denkleminin analitik çözümünün sıcaklık değişiminin tahmininde uygulanabilirliği incelenmiştir. Çözümün açıklanmasında herhangi toprak katmanı yüzeyinde ısı akışını ve ortalama toprak sıcaklığını içeren sınır koşulları kullanılmıştır. Analitik çözüme göre hesaplanan toprak sıcaklıkları ile ölçülen sıcaklıklar arasındaki HKOK, NHKOK, Dİ ve d istatistiksel göstergeler İnseptisol toprakta sırasıyla 1.339, 0.077, 0.576, 0.023; Entisol toprakta ise 0.654, 0.034, 0.067, 0.005 olarak saptanmıştır. İstatistiksel göstergelerin güvenilirlik sınırları dahilindeki değişimi, çözümün uygulanabilirliğinin mümkünlügünü göstermektedir. Investigation of the time-independent one-dimensional thermal conductivity of the soilAnahtar Sözcükler: Toprak sıcaklığı Toptağın ısısal özellikleri Sabit ısı iletkenliği denklemi Matematiksel modelleme ABSTRACT Depending on some thermal properties of soils, determining the time-independent temperature range is necessary for improving and preserving soil fertility, and creating optimum conditions for plant growth. Heat at the surface of any soil layer for explaining the solution was used from boundary conditions including flow and mean soil temperature. The statistical indicators of RMSE, NRMSE, DI and d between the measured soil temperatures according to the analytical solution were 1.339, 0.077, 0.576, 0.023 in Inseptisol soil, respectively; in Entisol soil was determined as 0.654, 0.034, 0.067, 0.005. The change of statistical indicators within the limits of reliability shows the feasibility of the solution.

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Investigating the capability of estimating soil thermal conductivity using topographical attributes for the Southern Great Plains, USA

Cited by 14 publications

References 50 publications

Pore unit cell network modeling of the thermal conductivity dynamics in unsaturated sandy soils: Unveiling the role of spanning‐wetting phase cluster

Pore unit cell network modeling of the thermal conductivity dynamics in unsaturated sandy soils: Unveiling the role of spanning‐wetting phase cluster

Neuro-Particle Swarm Optimization Based In-Situ Prediction Model for Heavy Metals Concentration in Groundwater and Surface Water

Toprağın zamana bağlı olmayan bir boyutlu ısı iletkenliğinin incelenmesi

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