Search results for "ridge regression"
showing 5 items of 5 documents
Seasonal patterns of biodiversity in Mediterranean coastal lagoons
2019
Aim: Understanding and quantifying the seasonal patterns in biodiversity of phyto- benthos, macro-zoobenthos and fishes in Mediterranean coastal lagoons, and the species dependence upon environmental factors. Location: The study was carried out in the “Stagnone di Marsala e Saline di Trapani e Paceco,” the largest coastal lagoon system in the central Mediterranean Sea (Sicily, Italy), a Special Protection Area located along one of the central ecological corridors joining Africa and Europe. Methods: The coastal lagoon system was selected as a model ecosystem to investi- gate the seasonal variations in biodiversity indices and dominance–diversity relation- ships in phytobenthos, macro-zoobent…
Análisis de métodos de validación cruzada para la obtención robusta de parámetros biofísicos
2015
[EN] Non-parametric regression methods are powerful statistical methods to retrieve biophysical parameters from remote sensing measurements. However, their performance can be affected by what has been presented during the training phase. To ensure robust retrievals, various cross-validation sub-sampling methods are often used, which allow to evaluate the model with subsets of the field dataset. Here, two types of cross-validation techniques were analyzed in the development of non-parametric regression models: hold-out and k-fold. Selected non-parametric linear regression methods were least squares Linear Regression (LR) and Partial Least Squares Regression (PLSR), and nonlinear methods were…
Synergistic integration of optical and microwave satellite data for crop yield estimation
2019
Developing accurate models of crop stress, phenology and productivity is of paramount importance, given the increasing need of food. Earth observation (EO) remote sensing data provides a unique source of information to monitor crops in a temporally resolved and spatially explicit way. In this study, we propose the combination of multisensor (optical and microwave) remote sensing data for crop yield estimation and forecasting using two novel approaches. We first propose the lag between Enhanced Vegetation Index (EVI) derived from MODIS and Vegetation Optical Depth (VOD) derived from SMAP as a new joint metric combining the information from the two satellite sensors in a unique feature or des…
Mapping daily global solar irradiation over Spain: A comparative study of selected approaches
2011
Abstract Three methods to estimate the daily global solar irradiation are compared: the Bristow–Campbell (BC), Artificial Neural Network (ANN) and Kernel Ridge Regression (KRR). BC is an empirical approach based on air maximum and minimum temperature. ANN and KRR are non-linear approaches that use temperature and precipitation data (which have been selected as the best combination of input data from a gamma test). The experimental dataset includes 4 years (2005–2008) of daily irradiation collected at 40 stations and temperature and precipitation data collected at 400 stations over Spain. Results show that the ANN method produces the best global solar irradiation estimates, with a mean absol…
Penalization and data reduction of auxiliary variables in survey sampling
2012
Survey sampling techniques are quite useful in a way to estimate population parameterssuch as the population total when the large dimensional auxiliary data setis available. This thesis deals with the estimation of population total in presenceof ill-conditioned large data set.In the first chapter, we give some basic definitions that will be used in thelater chapters. The Horvitz-Thompson estimator is defined as an estimator whichdoes not use auxiliary variables. Along with, calibration technique is defined toincorporate the auxiliary variables for sake of improvement in the estimation ofpopulation totals for a fixed sample size.The second chapter is a part of a review article about ridge re…