نتائج البحث

Generalized Skew Laplace Random Fields: Bayesian Spatial Prediction for Skew and Heavy Tailed Data

• Authors : Mohammad Mehdi Saber; Alireza Nematollahi; Mohsen Mohammadzadeh

Subjects: Bayesian spatial prediction; Multivariate generalized skew Laplace distribution; Metropolis–Hastings

• Source: Journal of Statistical Theory and Applications (JSTA), Vol 20, Iss 1 (2021)

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Improved soil carbon stock spatial prediction in a Mediterranean soil erosion site through robust machine learning techniques.

• Authors : Mosaid H; Geomatics, Georesources and Environment Laboratory, Faculty of Sciences and Techniques, Sultan Moulay Slimane University, Béni Mellal, Morocco. .; Barakat A

Subjects: Soil* ; Soil Erosion*; Carbon

• Source: Environmental monitoring and assessment [Environ Monit Assess] 2024 Jan 10; Vol. 196 (2), pp. 130. Date of Electronic Publication: 2024 Jan 10.Publisher: Springer Country of Publication: Netherlands NLM ID: 8508350 Publication Model: Electronic Cited Medium:

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Multilevel approximation of Gaussian random fields: Covariance compression, estimation, and spatial prediction

• Authors : Harbrecht, Helmut; Herrmann, Lukas; Kirchner, Kristin

Subjects: Matérn covariance; Multilevel monte carlo methods; Kriging

• Source: Advances in Computational Mathematics, 50 (5)

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Spatial prediction of soil organic carbon stocks in an arid rangeland using machine learning algorithms.

• Authors : Rostaminia M; Soil and Water Department, Agriculture Faculty, Ilam University, Ilam, Iran. .; Rahmani A

Subjects: Carbon*/Carbon*/Carbon*/analysis ; Soil*; Algorithms

• Source: Environmental monitoring and assessment [Environ Monit Assess] 2021 Nov 17; Vol. 193 (12), pp. 815. Date of Electronic Publication: 2021 Nov 17.Publisher: Springer Country of Publication: Netherlands NLM ID: 8508350 Publication Model: Electronic Cited Medium:

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Spatial prediction of soil depth using environmental covariates by quantile regression forest model.

• Authors : Lalitha M; ICAR-National Bureau of Soil Survey and Land Use Planning, Regional Centre, Bangalore, 560024, Karnataka, India. .; Dharumarajan S

Subjects: Ecosystem* ; Soil*; Environmental Monitoring

• Source: Environmental monitoring and assessment [Environ Monit Assess] 2021 Sep 18; Vol. 193 (10), pp. 660. Date of Electronic Publication: 2021 Sep 18.Publisher: Springer Country of Publication: Netherlands NLM ID: 8508350 Publication Model: Electronic Cited Medium:

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Mobile monitoring and spatial prediction of black carbon in Cairo, Egypt.

• Authors : Talaat H; Faculty of Engineering, Cairo University, Giza, 12631, Egypt.; Xu J

Subjects: Air Pollutants*/Air Pollutants*/Air Pollutants*/analysis ; Particulate Matter*/Particulate Matter*/Particulate Matter*/analysis; Carbon

• Source: Environmental monitoring and assessment [Environ Monit Assess] 2021 Aug 20; Vol. 193 (9), pp. 587. Date of Electronic Publication: 2021 Aug 20.Publisher: Springer Country of Publication: Netherlands NLM ID: 8508350 Publication Model: Electronic Cited Medium:

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Spatial Prediction and Digital Mapping of Soil Texture Classes in a Floodplain Using Multinomial Logistic Regression

• Authors : Kaya, Fuat; Başayiğit, Levent

Subjects: Digital soil mapping; Machine learning algorithms; Soil texture class

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Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree

• Authors : Bui, Dieu Tien; Tran, Anh Tuan; Klempe, Harald

Subjects: Article; PeerReviewed

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Spatial prediction of landslide hazard at the Luxi area (China) using support vector machines

• Authors : Hong, Haoyuan; Pradhan, Biswajeet; Jebur, Mustafa Neamah

Subjects: Article; PeerReviewed

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Bayesian data fusion in a spatial prediction context: a general formulation

• Authors : UCL - AGRO/MILA - Département des sciences du milieu et de l'aménagement du territoire; Bogaert, Patrick; Fasbender, Dominique

Subjects: data merging; measurement errors; soft information

• Source: Stochastic Environmental Research and Risk Assessment, Vol. 21, no. 6, p. 695-709 (2007)

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