Search results for "predictive model"
showing 10 items of 74 documents
Estimating “land use heritage” to model changes in archaeological settlement patterns
2016
International audience; In this paper, we present a method to calculate a “land use heritage map” based on the concept of “memory of landscape”. Such a map can be seen as one variable among others influencing site location preference, and can be used as input for predictive models. The computed values equate to an index of long-term land use intensity. We will first discuss the method used for creating the land use heritage map, for which kernel density estimates are used.We will then present the use of these land use heritage maps for site location analysis in two study areas in SE France. Earlier analyses showed that the influence of the natural environment on settlement location choice i…
Introducing the Human Factor in Predictive Modelling: a Work in Progress
2012
International audience; In this paper we present the results of a study into integrating socio-cultural factors into predictive modelling. So far, predictive modelling has largely neglected the social and cultural dimensions of past landscapes. To maintain its value for archaeological research, therefore, it needs new methodologies, concepts and theories. For this study, we have departed from the methodology developed in the 1990s during the Archaeomedes Project. In this project, cross-regional comparisons of settlement location factors were made by analyzing the environmental context of Roman settlements in the French Rhône Valley. For the current research, we expanded the set of variables…
A bark beetle infestation predictive model based on satellite data in the frame of decision support system TANABBO
2020
The European spruce bark beetle Ips typographus L. causes significant economic losses in managed coniferous forests in Central and Northern Europe. New infestations either occur in previously undisturbed forest stands (i.e., spot initiation) or depend on proximity to previous years’ infestations (i.e., spot spreading). Early identification of newly infested trees over the forested landscape limits the effective control measures. Accurate forecasting of the spread of bark beetle infestation is crucial to plan efficient sanitation felling of infested trees and prevent further propagation of beetle-induced tree mortality. We created a predictive model of subsequent year spot initiation and spo…
Predictive distribution models of European hake in the south-central Mediterranean Sea
2017
The effective management and conservation of fishery resources requires knowledge of their spatial distribution and notably of their critical life history stages. Predictive modelling of the European hake (Merluccius merluccius L., 1758) distribution was developed in the south-central Mediterranean Sea by means of historical fisheries-independent databases available in the region. The study area included the international waters of the south-central Mediterranean Sea and the territorial waters of Italy, Malta, Tunisia and Libya. Distribution maps of predicted population abundance index, and probabilistic occurrence of recruits and large adults were obtained by means of generalized additive …
Predicting shifting sustainability trade-offs in marine finfish aquaculture under climate change
2018
Defining sustainability goals is a crucial but difficult task because it often involves the quantification of multiple interrelated and sometimes conflicting components. This complexity may be exacerbated by climate change, which will increase environmental vulnerability in aquaculture and potentially compromise the ability to meet the needs of a growing human population. Here, we developed an approach to inform sustainable aquaculture by quantifying spatio-temporal shifts in critical trade-offs between environmental costs and benefits using the time to reach the commercial size as a possible proxy of economic implications of aquaculture under climate change. Our results indicate that optim…
Macrophytes in boreal streams: Characterizing and predicting native occurrence and abundance to assess human impact
2016
Abstract Macrophytes are a structurally and functionally essential element of stream ecosystems and therefore indispensable in assessment, protection and restoration of streams. Modelling based on continuous environmental gradients offers a potential approach to predict natural variability of communities and thereby improve detection of anthropogenic community change. Using data from minimally disturbed streams, we described natural macrophyte assemblages in pool and riffle habitats separately and in combination, and explored their variation across large scale environmental gradients. Specifically, we developed RIVPACS-type models to predict the presence and abundance of macrophyte taxa at …
Predictive Model Markup Language (PMML) Representation of Bayesian Networks: An Application in Manufacturing
2018
International audience; Bayesian networks (BNs) represent a promising approach for the aggregation of multiple uncertainty sources in manufacturing networks and other engineering systems for the purposes of uncertainty quantification, risk analysis, and quality control. A standardized representation for BN models will aid in their communication and exchange across the web. This article presents an extension to the predictive model markup language (PMML) standard for the representation of a BN, which may consist of discrete variables, continuous variables, or their combination. The PMML standard is based on extensible markup language (XML) and used for the representation of analytical models…
Opportunities for the Use of Business Data Analysis Technologies
2016
Abstract The paper analyses the business data analysis technologies, provides their classification and considers relevant terminology. The feasibility of business data analysis technologies handling big data sources is overviewed. The paper shows the results of examination of the online big data source analytics technologies, data mining and predictive modelling technologies and their trends.
Model-based Engineering for the Integration of Manufacturing Systems with Advanced Analytics
2016
To employ data analytics effectively and efficiently on manufacturing systems, engineers and data scientists need to collaborate closely to bring their domain knowledge together. In this paper, we introduce a domain-specific modeling approach to integrate a manufacturing system model with advanced analytics, in particular neural networks, to model predictions. Our approach combines a set of meta-models and transformation rules based on the domain knowledge of manufacturing engineers and data scientists. Our approach uses a model of a manufacturing process and its associated data as inputs, and generates a trained neural network model as an output to predict a quantity of interest. This pape…
Dynamic mean absolute error as new measure for assessing forecasting errors
2018
Abstract Accurate wind power forecast is essential for grid integration, system planning, and electricity trading in certain electricity markets. Therefore, analyzing prediction errors is a critical task that allows a comparison of prediction models and the selection of the most suitable model. In this work, the temporal error and absolute magnitude error are simultaneously considered to assess the forecast error. The trade-off between both types of errors is computed, analyzed, and interpreted. Moreover, a new index, the dynamic mean absolute error, DMAE, is defined to measure the prediction accuracy. This index accounts for both error components: temporal and absolute. Real cases of wind …