Search results for "Hybrid model"
showing 10 items of 17 documents
Top-of-Atmosphere Retrieval of Multiple Crop Traits Using Variational Heteroscedastic Gaussian Processes within a Hybrid Workflow.
2021
In support of cropland monitoring, operational Copernicus Sentinel-2 (S2) data became available globally and can be explored for the retrieval of important crop traits. Based on a hybrid workflow, retrieval models for six essential biochemical and biophysical crop traits were developed for both S2 bottom-of-atmosphere (BOA) L2A and S2 top-of-atmosphere (TOA) L1C data. A variational heteroscedastic Gaussian process regression (VHGPR) algorithm was trained with simulations generated by the combined leaf-canopy reflectance model PROSAILat the BOA scale and further combined with the Second Simulation of a Satellite Signal in the Solar Spectrum (6SV) atmosphere model at the TOA scale. Establishe…
Coupling agent-based with equation-based models to study spatially explicit megapopulation dynamics
2018
International audience; The incorporation of the spatial heterogeneity of real landscapes into population dynamics remains extremely difficult. We propose combining equation-based modelling (EBM) and agent-based modelling (ABM) to overcome the difficulties classically encountered. ABM facilitates the description of entities that act according to specific rules evolving on various scales. However, a large number of entities may lead to computational difficulties (e.g., for populations of small mammals, such as voles, that can exceed millions of individuals). Here, EBM handles age-structured population growth, and ABM represents the spreading of voles on large scales. Simulations applied to t…
Monitoring Cropland Phenology on Google Earth Engine Using Gaussian Process Regression
2021
Monitoring cropland phenology from optical satellite data remains a challenging task due to the influence of clouds and atmospheric artifacts. Therefore, measures need to be taken to overcome these challenges and gain better knowledge of crop dynamics. The arrival of cloud computing platforms such as Google Earth Engine (GEE) has enabled us to propose a Sentinel-2 (S2) phenology end-to-end processing chain. To achieve this, the following pipeline was implemented: (1) the building of hybrid Gaussian Process Regression (GPR) retrieval models of crop traits optimized with active learning, (2) implementation of these models on GEE (3) generation of spatiotemporally continuous maps and time seri…
A new hybrid method to improve the ultra-short-term prediction of LOD
2019
Accurate, short-term predictions of Earth orientation parameters (EOP) are needed for many real-time applications including precise tracking and navigation of interplanetary spacecraft, climate forecasting, and disaster prevention. Out of the EOP, the LOD (length of day), which represents the changes in the Earth’s rotation rate, is the most challenging to predict since it is largely affected by the torques associated with changes in atmospheric circulation. In this study, the combination of Copula-based analysis and singular spectrum analysis (SSA) method is introduced to improve the accuracy of the forecasted LOD. The procedure operates as follows: First, we derive the dependence structur…
Landslide susceptibility mapping using precipitation data, Mazandaran Province, north of Iran
2017
Precipitation is a nonlinear and complex phenomenon and varies in time and space. It is also evident that there is a link between precipitation and shallow landslides, and precipitation is always considered as a landslide-triggering factor. This study aims to investigate the relationship between the characteristics of precipitation and the historical shallow landslides in Mazandaran Province, north of Iran. For this purpose, the spatial variability of rainfall was analyzed using monthly rainfall data collected at 15 synoptic stations distributed over the region between 1981 and 2014. Monthly precipitation and other derived parameters were used, and a hybrid model combining principal compone…
Advancing Deep Learning for Earth Sciences: From Hybrid Modeling to Interpretability
2020
Machine learning and deep learning in particular have made a huge impact in many fields of science and engineering. In the last decade, advanced deep learning methods have been developed and applied to remote sensing and geoscientific data problems extensively. Applications on classification and parameter retrieval are making a difference: methods are very accurate, can handle large amounts of data, and can deal with spatial and temporal data structures efficiently. Nevertheless, several important challenges need still to be addressed. First, current standard deep architectures cannot deal with long-range dependencies so distant driving processes (in space or time) are not captured, and the…
Polar motion prediction using the combination of SSA and Copula-based analysis
2018
The real-time estimation of polar motion (PM) is needed for the navigation of Earth satellite and interplanetary spacecraft. However, it is impossible to have real-time information due to the complexity of the measurement model and data processing. Various prediction methods have been developed. However, the accuracy of PM prediction is still not satisfactory even for a few days in the future. Therefore, new techniques or a combination of the existing methods need to be investigated for improving the accuracy of the predicted PM. There is a well-introduced method called Copula, and we want to combine it with singular spectrum analysis (SSA) method for PM prediction. In this study, first, we…
The Post-entrepreneurial University: The Case for Resilience in Higher Education
2021
AbstractHistorically speaking, the university has been a highly resilient organizational form; however recent pressures to become entrepreneurial threaten the institutional foundations on which that reliance is based. The chapter first provides conceptual clarity by revisiting what we argue are two distinct schools of thought on the entrepreneurial university. We show how the economic school’s conception intertwines with the rise of New Public Management (NPM) in Europe in the late 1990s and early 2000s, reframing the concept in ways that made it incompatible with resilience thinking. However, we argue that by tying back into ‘lost’ elements of sociological school’s conception, and associat…
A hybrid scheme for action representation
1993
Strong deficiencies are present in symbolic models for action representation and planning, regarding mainly the difficulty of coping with real, complex environments. These deficiencies can be attributed to several problems, such as the inadequacy in coping with incompletely structured situations, the difficulty of interacting with visual and motorial aspects, the difficulty in representing low-level knowledge, the need to specify the problem at a high level of detail, and so on. Besides the purely symbolic approaches, several nonsymbolic models have been developed, such as the recent class of subsym-bolic techniques. A promising paradigm for the modeling of reasoning, which combines feature…
Estimation of daily average values of the Ångström turbidity coefficient β using a Corrected Yang Hybrid Model
2013
This paper aims to test a method for estimating daily values of atmospheric turbidity from non-specialized data, such as those obtained from agro-meteorological stations. This method allows estimating the spatial and temporal evolution of aerosols concentration in more places than just those in which direct measurements are available. The method is based on the Corrected Yang Hybrid Model (CYHM). The data used in the determination of errors between measured and estimated values of the daily Angstrom turbidity coefficient β were recorded in Valencia, Spain, during 2009 and 2011. These data were global solar irradiance, direct solar irradiance, temperature, relative humidity and Aerosol Optic…