In this work, we present a multi-temporal uncertainty-based method that incorporates regression models to establish the risk (probability) of land cover change (RLCC), as a function of a set of environmental and socioeconomic driving factors.
We apply a remote-sensing (RS -based method that incoRS -based11 dimethodnal feathats of Thematincorporatesd Enhanced Thematic Mapper Plus (TM/ETM+) images to map changes in urban land, and also develop a series of multi-dimensional indicators within a drivers, pressures, states, impacts, and responses (DPSIR) framework to quantify urban land expansion.
Moreover, regular expression-based methods that incorporate a simple scoring scheme are proposed [ 17- 19].
Miranda predicts microRNA targets based on a three phase method that incorporates sequence-matching to estimate the complementarity between a miRNA and a potential target gene to give a score (S).
Quantification was based on the window-of-linearity method that incorporates individual PCR efficiencies for each sample.
The developed model was based on Comsol Multiphysics® a finite element method that incorporates multi-phase flow, multi-component transport and non-equilibrium transient mass transfer.
The method that incorporates deep object features [35] remains competitive against our proposed methods.
31 A more complex method that incorporates both a priori available information about the RA prevalence in Italy and empirical misclassification was also tested in order to improve the estimation based on the current knowledge.
We present a method that incorporates spatial information by means of tailored, probability distributed time-delays.
Results: We present S4VDPCA, a sparse PCA method that incorporates a subsampling approach, namely stability selection.
Modern pattern-based geostatistical methods that incorporate higher-order spatial statistics are more suitable for describing such complex spatial patterns.
