Results 61 to 70 of about 1,562 (177)

From Resistivity to Hydraulic Properties: Calibrating a Groundwater Flow Model by Integrating Airborne Electromagnetic and Borehole Data Into a Probabilistic Multi‐Texture Framework

Water Resources Research, Volume 62, Issue 8, August 2026.
Abstract Airborne electromagnetic (AEM) surveys offer rapid, cost‐effective subsurface imaging, yet converting their electrical resistivity (ER) models into physically meaningful hydraulic property fields for groundwater models remains a challenge. We develop and demonstrate a data‐driven workflow for an unconsolidated sedimentary aquifer system in ...
Leland Scantlebury, Thomas Harter
wiley   +1 more source

Atmospheric Constituent Data Assimilation in NASA's Goddard Earth Observing System

Earth and Space Science, Volume 13, Issue 8, August 2026.
Abstract Many of today's most significant and urgent scientific questions, including those about the impact of human activity on air quality and radiative forcing, require the synthesis of observations of atmospheric constituents and scientific theory.
B. Weir, K. Wargan, C. A. Keller, K. E. Knowland, P. A. Wales, V. Shah, B. M. Karpowicz, N. Balashov, L. E. Ott, R. Todling, S. E. Cohn, S. Pawson   +11 more
wiley   +1 more source

Conformal prediction for functional Ordinary kriging

Functional Ordinary Kriging is the most widely used method to predict a curve at a given spatial point. However, uncertainty remains an open issue. In this article a distribution-free prediction method based on two different modulation functions and two conformity scores is proposed.
De Magistris, Anna, Diana, Andrea, Romano, Elvira   +2 more
openaire   +2 more sources

Kriging with trend functions nonlinear in their parameters: Theory and application in enzyme kinetics [PDF]

Engineering in Life Sciences, 2017
Kriging is an interpolation method commonly applied in empirical modeling for approximating functional relationships between impact factors and system response. The interpolation is based on a statistical analysis of given data and can optionally include a priori defined trend functions.
Lars, Freier, Wolfgang, Wiechert, Eric, von Lieres   +2 more
openaire   +2 more sources

CESAR: A Convolutional Echo State AutoencodeR for High‐Resolution Wind Forecasting

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 4, August 2026.
Abstract An accurate and timely assessment of wind speed and energy output allows an efficient planning and management of this resource on the power grid. Wind energy, especially at high resolution, calls for the development of nonlinear statistical models able to capture complex dependencies in space and time. This work introduces a Convolutional Echo
Matthew Bonas, Paolo Giani, Paola Crippa, Stefano Castruccio   +3 more
wiley   +1 more source

A DEM-PSO-Kriging framework for minimizing mechanical damage and nutrient pollution from controlled release fertilizers

Industrial Crops and Products
Controlled release fertilizers constitute a high-value segment of industrial crop products. However, their efficacy in sustainable agriculture relies heavily on functional integrity, which is often compromised by mechanical damage during application ...
Yangyang Kong, Yikun Pei, Zhenwei Tong, Peilin Zhou, Yong Chen, Xiuli Zhang   +5 more
doaj   +1 more source

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

Conservation Biology, Volume 40, Issue 4, August 2026.
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

Extending Functional kriging to a multivariate context

, 2020
Environmental data usually have a spatio-temporal structure; pollutant concentrations, for example, are recorded along time and space. Generalized Additive Models (GAMs) represent a suitable tool to model spatial and/or temporal trends of this kind of data, that can be treated as functional, although they are collected as discrete observations ...
francesca di salvo, mariantonietta ruggieri, antonella plaia   +2 more
openaire   +2 more sources

Mapping Coral Reef Resilience Indicators Using Field and Remotely Sensed Data

Remote Sensing, 2013
In the face of increasing climate-related impacts on coral reefs, the integration of ecosystem resilience into marine conservation planning has become a priority.
Stuart Phinn, Mitchell Lyons, Chris Roelfsema, Stacy Jupiter, Anders Knudby   +4 more
doaj   +1 more source

Key sources of uncertainty in process‐based modeling of live fuel moisture content

New Phytologist, Volume 251, Issue 4, Page 1723-1737, August 2026.
Location of the selected live fuel moisture content (LFMC) sampling sites. Summary Process‐based models that mechanistically represent water‐carbon balances in the atmosphere‐soil–plant continuum are an attractive tool for monitoring live fuel moisture content (LFMC) dynamics, a key variable when assessing fire danger.
Rodrigo Balaguer‐Romano, Albert Sañé, Nicolas Martin‐StPaul, Julien Ruffault, Eva Gabriel, Xavier Castro, François Pimont, Xiangzhuo Liu, Arsène Druel, Sylvain Delzon, Miquel De Cáceres   +10 more
wiley   +1 more source