Results 81 to 90 of about 56,902 (196)

Structured machine learning modeling to support conservation of deep‐sea benthic biodiversity

Conservation Biology, EarlyView.
Abstract Biodiversity monitoring programs need to deliver accurate, timely, and actionable predictions. To establish a predictive monitoring program for deep‐sea benthos of the Santos Basin, Brazil, we developed a two‐stage structured model that allowed comparison of biodiversity predictions obtained from environmental simulations (2M‐Sim).
Gustavo Fonseca, Danilo C. Vieira, Juliane C. Carneiro, Renato S. Carreira, Milena Ceccopieri, Thais N. Corbisier, Adriana Galindo Dalto, Alberto G. Figueiredo Jr., Fabiane Gallucci, Paula Gheller, Simone Brito de Jesus, Helena Passeri Lavrado, Letícia Lazzari, Eduardo Hilzendeger Marcon, Daniel Leite Moreira, Rafael Bendayan de Moura, Ellen Pape, Ana Cláudia Aoki Santarosa, João Regis dos Santos Filho, Silvia Helena de Mello e Sousa, Thaisa Marques Vicente, Luciana Erika Yaginuma, Cinthia Yamashita, Wandrey Watanabe   +23 more
wiley   +1 more source

Subsurface Geological Profile Interpolation Using a Fractional Kriging Method Enhanced by Random Forest Regression

Fractal and Fractional
Analyzing geological profiles is of great importance for various applications such as natural resource management, environmental assessment, and mining engineering projects.
Qile Ding, Yiren Wang, Yu Zheng, Fengyang Wang, Shudong Zhou, Donghui Pan, Yuchun Xiong, Yi Zhang   +7 more
doaj   +1 more source

Investigation of Spatial Variation of Some Soil Properties Using Geostatistical Methods (Case study: Margon Town, Kohgiluyeh and Boyer-Ahmad Province, Iran) [PDF]

محیط زیست و مهندسی آب
The aim of this study was to investigate the spatial variation of some soil properties such as soil texture, organic carbon content, soil pH and electrical conductivity (EC) using geostatistical methods in Margon town, Kohgiluyeh and Boyer-Ahmad province,
Vali Behnam, Ali Karami, Sedigheh , Safarzadeh Shirazi   +2 more
doaj   +1 more source

Ordinary kriging for on-demand average wind interpolation of in-situ wind sensor data

, 2009
We have developed a domain agnostic ordinary kriging algorithm accessible via a standards-based service-oriented architecture for sensor networks. We exploit the Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) standards.
Middleton, S.E., Veres, G., Zlatev, Z.
core  

Bayesian analysis of hierarchical multi-fidelity codes [PDF]

, 2012
This paper deals with the Gaussian process based approximation of a code which can be run at different levels of accuracy. This method, which is a particular case of co-kriging, allows us to improve a surrogate model of a complex computer code using fast
Gratiet, Loic Le
core   +2 more sources

Handling Out‐of‐Sample Areas to Estimate the Unemployment Rate at Local Labour Market Areas in Italy

International Statistical Review, EarlyView.
Summary Unemployment rate estimates for small areas are used to efficiently support the distribution of services and the allocation of resources, grants and funding. A Fay–Herriot type model is the most used tool to obtain these estimates. Under this approach out‐of‐sample areas require some synthetic estimates. As the geographical context is extremely
Roberto Benedetti, Federica Piersimoni, Monica Pratesi, Nicola Salvati, Thomas Suesse   +4 more
wiley   +1 more source

Monotonicity-Preserving Bootstrapped Kriging Metamodels for Expensive Simulations [PDF]

Kriging (Gaussian process, spatial correlation) metamodels approximate the Input/Output (I/O) functions implied by the underlying simulation models; such metamodels serve sensitivity analysis and optimization, especially for computationally expensive ...
Beers, W.C.M. van, Kleijnen, Jack P.C.
core   +1 more source

Additive Kernels for Gaussian Process Modeling [PDF]

, 2010
Gaussian Process (GP) models are often used as mathematical approximations of computationally expensive experiments. Provided that its kernel is suitably chosen and that enough data is available to obtain a reasonable fit of the simulator, a GP model can
Durrande, Nicolas, Ginsbourger, David, Roustant, Olivier   +2 more
core   +2 more sources

On Spatial Point Processes With Composition‐Valued Marks

International Statistical Review, EarlyView.
Summary Methods for marked spatial point processes with scalar marks have seen extensive development in recent years. While the impressive progress in data collection and storage capacities has yielded an immense increase in spatial point process data with highly challenging non‐scalar marks, methods for their analysis are not equally well developed ...
Matthias Eckardt, Mari Myllymäki, Sonja Greven   +2 more
wiley   +1 more source

Expected Improvement in Efficient Global Optimization Through Bootstrapped Kriging - Replaces CentER DP 2010-62 [PDF]

This article uses a sequentialized experimental design to select simulation input com- binations for global optimization, based on Kriging (also called Gaussian process or spatial correlation modeling); this Kriging is used to analyze the input/output ...
Beers, W.C.M. van, Kleijnen, Jack P.C., Nieuwenhuyse, I. van   +2 more
core   +1 more source