Estimation of soil temperature from meteorological data using different machine learning models

Feng, Yu; Cui, Ningbo; Hao, Weiping; Gao, Lili; Gong, Daozhi

doi:10.1016/j.geoderma.2018.11.044

Cited by 139 publications

(83 citation statements)

References 59 publications

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“…Only the use of a mixture of several reactive materials can ensure the removal of a larger spectrum of contaminants. The conclusion is consistent with those presented by other researchers [17,18,20,29,30]. The obtained test results indicate that the highest removal efficiency of Cu and PO 4 -P has activated carbon, of Zn-limestone sand, and of NH 4 -N-zeolite.…”

Section: Potential Application Of the Study Resultssupporting

confidence: 92%

The Impact of Temperature on the Removal of Inorganic Contaminants Typical of Urban Stormwater

Fronczyk

Mumford

2019

Applied Sciences

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Appropriate management of urban stormwater requires consideration of both water quantity, resulting from flood control requirements, and water quality, being a consequence of contaminant distribution via runoff water. This article focuses on the impact of temperature on the efficiency of stormwater treatment processes in permeable infiltration systems. Studies of the removal capacity of activated carbon, diatomite, halloysite, limestone sand and zeolite for select heavy metals (Cu and Zn) and biogenes (NH4-N and PO4-P) were performed in batch conditions at 3, 6, 10, 15, 22, 30 and 40 °C at low initial concentrations, and maximum sorption capacities determined at 3, 10, 22 and 40 °C. A decrease in temperature to 3 °C reduced the maximum sorption capacities (Qmax) of the applied materials in the range of 10% for diatomite uptake of PO4-P, to 46% for halloysite uptake of Cu. Only the value of Qmax for halloysite, limestone sand and diatomite for NH4-N uptake decreased slightly with temperature increase. A positive correlation was also observed for the equilibrium sorption (Qe) of Cu and Zn for analyses performed at low concentrations (with the exception of Zn sorption on limestone sand). In turn, for biogenes a rising trend was observed only in the range of 3 °C to 22 °C, whereas further temperature increase caused a decrease of Qe. Temperature had the largest influence on the removal of copper and the smallest on the removal of phosphates. It was also observed that the impact of temperature on the process of phosphate removal on all materials and ammonium ions on all materials, with the exception of zeolite, was negligible.

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Section: Potential Application Of the Study Resultssupporting

confidence: 92%

The Impact of Temperature on the Removal of Inorganic Contaminants Typical of Urban Stormwater

Fronczyk

Mumford

2019

Applied Sciences

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“…The other may be the different behavior of ST at 100 depth as also observed from Fig 2. In overall, the ELM provides better accuracy than the ANN, CART, GMDH and MLR in estimating soil temperature at different multiple depths. This result is in accordance with the study of Feng et al [19] in which ELM was applied in estimating soil temperature at the depths of 2, 5, 10 and 20 cm and compared with GRNN, BPNN and RF models. Better estimates were obtained from ELM compared to other models.…”

Section: Plos Onesupporting

confidence: 91%

“…The implementation of these procedures several are definitely costly and time-consuming especially in developing countries [18]. As a result, the accessibility of accurate and consistent ST data are very limited and hence there a need for robust model that able to capture the mapping between the input(s) and the ST as the model's output Feng et al [19]. Recently, Mehdizadeh et al [20] developed Fractionally Autoregressive Integrated Moving Average (FARIMA) model so as to predict the ST and compare the results with classical Artificial Intelligent (AI) models namely; Gene Expression Programming (GEP) and Feed Forward Back Propagation Neural Network (FFBPNN) methods.…”

Section: Introductionmentioning

confidence: 99%

Advanced machine learning model for better prediction accuracy of soil temperature at different depths

et al. 2020

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Soil temperature has a vital importance in biological, physical and chemical processes of terrestrial ecosystem and its modeling at different depths is very important for land-atmosphere interactions. The study compares four machine learning techniques, extreme learning machine (ELM), artificial neural networks (ANN), classification and regression trees (CART) and group method of data handling (GMDH) in estimating monthly soil temperatures at four different depths. Various combinations of climatic variables are utilized as input to the developed models. The models' outcomes are also compared with multi-linear regression based on Nash-Sutcliffe efficiency, root mean square error, and coefficient of determination statistics. ELM is found to be generally performs better than the other four alternatives in estimating soil temperatures. A decrease in performance of the models is observed by an increase in soil depth. It is found that soil temperatures at three depths (5, 10 and 50 cm) could be mapped utilizing only air temperature data as input while solar radiation and wind speed information are also required for estimating soil temperature at the depth of 100 cm.

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“…The training dataset is used to learn the parameters, the validation dataset to compare models fitted with different hyper-parameters in order to find the optimal combination, and the test dataset as the independent, unseen data. In soil sciences, ML algorithms are usually trained using the traditional train/validation split or cross-validation (Keskin et al, 2019;Liang et al, 2019), or even no validation (Feng et al, 2019), except for some studies based on DL or with engineering background (e.g. Reale et al (2018)), including some of our publications on the use of DL for DSM (Padarian et al, 2019c) or soil spectroscopy (Padarian et al, 2019b, a), which use a train/validation/test split.…”

Section: New Good Practicesmentioning

confidence: 99%

Machine learning and soil sciences: A review aided by machine learning tools

Padarian¹,

Minasny²,

McBratney³

2019

Preprint

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Abstract. The application of machine learning (ML) techniques in various fields of science has increased rapidly, especially in the last ten years. The increasing availability of soil data that can be efficiently acquired remotely and proximally, and freely available open-source algorithms, have led to an accelerated adoption of ML techniques to analyse soil data. Given the large number of publications, it is an impossible task to manually review all papers on the application of ML in soil science without narrowing down a narrative of ML application in a specific research question. This paper aims to provide a comprehensive review of the application of ML techniques in soil science aided by a ML algorithm (Latent Dirichlet Allocation) to find patterns in a large collection of text corpus. The objective is to gain insight into publications of ML applications in soil science and to discuss the research gaps in this topic. We found that: a) there is an increasing usage of ML methods in soil sciences, mostly concentrated in developed countries, b) the reviewed publication can be grouped into 12 topics, namely remote sensing, soil organic carbon, water, contamination, methods (ensembles), erosion and parent material, methods (NN, SVM), spectroscopy, modelling (classes), crops, physical and modelling (continuous), c) advanced ML methods usually perform better than simpler approaches thanks to their capability to capture non-linear relationships. From these findings, we found research gaps, in particular: about the precautions that should be taken (parsimony) to avoid overfitting, and that the interpretability of the ML models is an important aspect to consider when applying advanced ML methods in order to improve our knowledge and understanding of soil. We foresee that a large number of studies will focus on the latter topic.

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Estimation of soil temperature from meteorological data using different machine learning models

Cited by 139 publications

References 59 publications

The Impact of Temperature on the Removal of Inorganic Contaminants Typical of Urban Stormwater

The Impact of Temperature on the Removal of Inorganic Contaminants Typical of Urban Stormwater

Advanced machine learning model for better prediction accuracy of soil temperature at different depths

Machine learning and soil sciences: A review aided by machine learning tools

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