Search results for "Predictive Model"

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…

Predictive models for energy saving in Wireless Sensor Networks

2011

ICT devices nowadays cannot disregard optimizations toward energy sustainability. Wireless Sensor Networks, in particular, are a representative class of a technology where special care must be given to energy saving, due to the typical scarcity and non-renewability of their energy sources, in order to enhance network lifetime. In our work we propose a novel approach that aims to adaptively control the sampling rate of wireless sensor nodes using prediction models, so that environmental phenomena can be consistently modeled while reducing the required amount of transmissions; the approach is tested on data available from a public dataset.

Gaussian Process Regression (GPR) Representation in Predictive Model Markup Language (PMML)

2017

International audience; This paper describes Gaussian process regression (GPR) models presented in predictive model markup language (PMML). PMML is an extensible-markup-language (XML) -based standard language used to represent data-mining and predictive analytic models, as well as pre- and post-processed data. The previous PMML version, PMML 4.2, did not provide capabilities for representing probabilistic (stochastic) machine-learning algorithms that are widely used for constructing predictive models taking the associated uncertainties into consideration. The newly released PMML version 4.3, which includes the GPR model, provides new features: confidence bounds and distribution for the pred…

Using the concept of spatial contexts for the prediction of archaeological rural settlement

2011

An empirical assessment of the Feltham-Ohlson models considering the sign of abnormal earnings

2006

Abstract This paper provides an empirical assessment of the Feltham-Ohlson models, distinguishing between firms with positive and negative abnormal earnings. Abnormal earnings persistence and conservatism parameters differ for these two groups; this implies different earnings prediction models and valuation functions for both profit-making and loss-making firms. The analysis refers to the period 1991-1999 and uses a sample of Spanish firms quoted on the Madrid S.E. The results suggest that our contextual approach is more useful than the non-contextual one to predict future abnormal earnings and explain current prices. Although the Ohlson (1995) model is accurate in forecasting future abnorm…

External parameters contribution in domestic load forecasting using neural network

2015

Domestic demand prediction is very important for home energy management system and also for peak reduction in the power system network. In this work, for precise prediction of power demand, external parameters, such as temperature and solar radiation, are considered and included in the prediction model for improving prediction performance. Power prediction models for coming hours' power demand estimation are built using neural network based on hourly power consumptions data with / without ambient temperature data and global solar irradiation (GSI) data respectively. In this work, a typical Southern Norwegian household demand has been considered. As a result, both ambient temperature and GSI…

How to formulate an accident prediction model for urban intersections.

2009

Several safety prediction models and methods have been developed to eliminate the relationship between the expected accident frequency and various urban intersection geometry and operational attributes. It is generally accepted that accident rates tend to be higher at intersections than on through sections of a road. This is particularly frequent in urban area where roads are characterized by intersections in close succession; moreover, the safe and effective operations of the urban road system can be significantly affected by safety conditions at intersections. In this paper models and methods designed to understand and to predict the accident process at urban intersections are reviewed. I…

Development Of An Econometric Model Case Study: Romanian Classification System

2015

Abstract The purpose of the paper is to illustrate an econometric model used to predict the lean meat content in pig carcasses, based on the muscle thickness and back fat thickness measured by the means of an optical probe (OptiGrade PRO).The analysis goes through all steps involved in the development of the model: statement of theory, specification of the mathematical model, sampling and collection of data, estimation of the parameters of the chosen econometric model, tests of the hypothesis derived from the model and prediction equations. The data have been in a controlled experiment conducted by the Romanian Carcass Classification Commission in 2007. The purpose of the experiment was to …

Environmental suitability model for the lanner falcon Falco biarmicus feldeggii: planning, study and monitoring the Sicilian population

2017

The identification of suitable areas, by spatially explicit distribution models, is crucial for conservation of threatened species as the lanner falcon Falco biarmicus feldeggii. Monitoring and collecting data on lanner falcon during years has proven to be essential for better defining the areas of species environmental suitability. Recent research shows that breeding performances of this species are strongly influenced by bioclimatic factors, especially monthly temperature and rainfall, or linked to landscape morphology, such as the slope of territories. These environmental parameters combined with species productivity (number of fledged juveniles per checked pair) of geo-referenced breedi…

Study on the application of an interspecific competition model for the prediction of microflora behaviour during the fermentation process of S. Angel…

2009

The use of predictive microbiology models able to evaluate bacterial behaviour as a function of environmental conditions and, at the same time, of natural microflora competition was considered by several authors with different approaches. Some authors modelled bacterial competition as a function of metabolic product with particular regard to lactic acid and modelled interspecific bacterial competition introducing a term into a conventional primary predictive model, which gives account for the interaction between two populations, so that they inhibit each other to the same extent that they inhibit their own growth.