Search results for "kriging"
showing 10 items of 93 documents
Treed Gaussian Process Regression for Solving Offline Data-Driven Continuous Multiobjective Optimization Problems
2023
Abstract For offline data-driven multiobjective optimization problems (MOPs), no new data is available during the optimization process. Approximation models (or surrogates) are first built using the provided offline data and an optimizer, e.g. a multiobjective evolutionary algorithm, can then be utilized to find Pareto optimal solutions to the problem with surrogates as objective functions. In contrast to online data-driven MOPs, these surrogates cannot be updated with new data and, hence, the approximation accuracy cannot be improved by considering new data during the optimization process. Gaussian process regression (GPR) models are widely used as surrogates because of their ability to pr…
An Interactive Framework for Offline Data-Driven Multiobjective Optimization
2020
We propose a framework for solving offline data-driven multiobjective optimization problems in an interactive manner. No new data becomes available when solving offline problems. We fit surrogate models to the data to enable optimization, which introduces uncertainty. The framework incorporates preference information from a decision maker in two aspects to direct the solution process. Firstly, the decision maker can guide the optimization by providing preferences for objectives. Secondly, the framework features a novel technique for the decision maker to also express preferences related to maximum acceptable uncertainty in the solutions as preferred ranges of uncertainty. In this way, the d…
Geoestadística en regiones heterogéneas con distancia basada en el coste
2012
El germen de la presente tesis consistió en un problema aplicado, de ingeniería, al que pensamos que la Estadística como disciplina puede contribuir de manera significativa. Concretamente, se trata de la elaboración de mapas acústicos en entornos urbanos. Resuelto habitualmente de una manera determinista y aproximada, la valoración de la incertidumbre de los resultados es extremadamente deficiente en la mayoría de los casos reales. Este problema, siendo de naturaleza espacial, se puede ver como un problema de predicción geoestadística, a partir de un conjunto de observaciones de campo. La dificultad radica en que el fenómeno se sitúa en un entorno urbano, que posee una importante heterogene…
Mesures de la température et spatialisation de l’Ilot de Chaleur Urbain à Dijon
2015
The Territorial Climate Energy Plan (PCET) of the agglomeration of Dijon (Grand Dijon) includes ameasurement campaign (6 June to 28 September 2014). 50 Hobo proV2 thermometers were deployed. The selection of siteswas carried out so that the different types of urban environment (Oke, 2006) are documented. The Urban Heat Island (UHI)is discernible mainly at night, when radiative conditions are well established the day before. It is estimated to 1°C onaverage for the summer, 3-4°C during nights of fine weather. It reached 6°C during the warmest periods of the 2014 summer.A cool axis through the agglomeration shows that vegetation and water can sensibly mitigate the ICU effect.
Sensitivity analysis of Gaussian processes for oceanic chlorophyll prediction
2015
Gaussian Process Regression (GPR) for machine learning has lately been successfully introduced for chlorophyll content mapping from remotely sensed data. The method provides a fast, stable and accurate prediction of biophysical parameters. However, since GPR is a non-linear kernel regression method, the relevance of the features are not accessible. In this paper, we introduce a probabilistic approach for feature sensitivity analysis (SA) of the GPR in order to reveal the relative importance of the features (bands) being used in the regression process. We evaluated the SA on GPR ocean chlorophyll content prediction. The method revealed the importance of the spectral bands, thus allowing the …
Statistical biophysical parameter retrieval and emulation with Gaussian processes
2019
Abstract Earth observation from satellites poses challenging problems where machine learning is being widely adopted as a key player. Perhaps the most challenging scenario that we are facing nowadays is to provide accurate estimates of particular variables of interest characterizing the Earth's surface. This chapter introduces some recent advances in statistical bio-geophysical parameter retrieval from satellite data. In particular, we will focus on Gaussian process regression (GPR) that has excelled in parameter estimation as well as in modeling complex radiative transfer processes. GPR is based on solid Bayesian statistics and generally yields efficient and accurate parameter estimates, a…
Global Sensitivity Analysis of Leaf-Canopy-Atmosphere RTMs: Implications for Biophysical Variables Retrieval from Top-of-Atmosphere Radiance Data.
2019
Knowledge of key variables driving the top of the atmosphere (TOA) radiance over a vegetated surface is an important step to derive biophysical variables from TOA radiance data, e.g., as observed by an optical satellite. Coupled leaf-canopy-atmosphere Radiative Transfer Models (RTMs) allow linking vegetation variables directly to the at-sensor TOA radiance measured. Global Sensitivity Analysis (GSA) of RTMs enables the computation of the total contribution of each input variable to the output variance. We determined the impacts of the leaf-canopy-atmosphere variables into TOA radiance using the GSA to gain insights into retrievable variables. The leaf and canopy RTM PROSAIL was coupled with…
Seasonal Mapping of Irrigated Winter Wheat Traits in Argentina with a Hybrid Retrieval Workflow Using Sentinel-2 Imagery
2022
Earth observation offers an unprecedented opportunity to monitor intensively cultivated areas providing key support to assess fertilizer needs and crop water uptake. Routinely, vegetation traits mapping can help farmers to monitor plant development along the crop’s phenological cycle, which is particularly relevant for irrigated agricultural areas. The high spatial and temporal resolution of the Sentinel-2 (S2) multispectral instrument leverages the possibility to estimate leaf area index (LAI), canopy chlorophyll content (CCC), and vegetation water content (VWC) from space. Therefore, our study presents a hybrid retrieval workflow combining a physically-based strategy with a machine learni…
Exploitation of SAR and Optical Sentinel Data to Detect Rice Crop and Estimate Seasonal Dynamics of Leaf Area Index
2017
This paper presents and evaluates multitemporal LAI estimates derived from Sentinel-2A data on rice cultivated area identified using time series of Sentinel-1A images over the main European rice districts for the 2016 crop season. This study combines the information conveyed by Sentinel-1A and Sentinel-2A into a high-resolution LAI retrieval chain. Rice crop was detected using an operational multi-temporal rule-based algorithm, and LAI estimates were obtained by inverting the PROSAIL radiative transfer model with Gaussian process regression. Direct validation was performed with in situ LAI measurements acquired in coordinated field campaigns in three countries (Italy, Spain and Greece). Res…
Comparative analysis of different techniques for spatial interpolation of rainfall data to create a serially complete monthly time series of precipit…
2011
Abstract The availability of good and reliable rainfall data is fundamental for most hydrological analyses and for the design and management of water resources systems. However, in practice, precipitation records often suffer from missing data values mainly due to malfunctioning of raingauge for specific time periods. This is an important issue in practical hydrology because it affects the continuity of rainfall data and ultimately influences the results of hydrologic studies which use rainfall as input. Many methods to estimate missing rainfall data have been proposed in literature and, among these, most are based on spatial interpolation algorithms. In this paper different spatial interpo…