A comparison of three learning methods to predict N2O fluxes and N leaching

International audienceThe environmental costs of intensive farming activities are often under-estimated or not included into rural development plans, even though they play an important role in addressing future society's needs. This paper focuses on the use of statistical learning methods to predict N2O emissions and N leaching under several conservative scenarios, in order to provide an alternative approach to deterministic models on a macro-scale. To that aim, three learning methods, namely neural networks (multilayer perceptrons), SVM and random forests, are compared and provide accurate solutions

A comparison of eight metamodeling techniques for the simulation of N2O fluxes and N leaching from corn crops

International audienceThe environmental costs of intensive farming activities are often under-estimated or not traded by the market, even though they play an important role in addressing future society's needs. The estimation of nitrogen (N) dynamics is thus an important issue which demands detailed simulation based methods and their integrated use to correctly represent complex and non-linear interactions into cropping systems. To calculate the N2O flux and N leaching from European arable lands, a modeling framework has been developed by linking the CAPRI agro-economic dataset with the DNDC-EUROPE bio-geo-chemical model. But, despite the great power of modern calculators, their use at continental scale is often too computationally costly. By comparing several statistical methods this paper aims to design a metamodel able to approximate the expensive code of the detailed modeling approach, devising the best compromise between estimation performance and simulation speed. We describe the use of two parametric (linear) models and six non-parametric approaches: two methods based on splines (ACOSSO and SDR), one method based on kriging (DACE), a neural networks method (multilayer perceptron, MLP), SVM and a bagging method (random forest, RF). This analysis shows that, as long as few data are available to train the model, splines approaches lead to best results, while when the size of training dataset increases, SVM and RF provide faster and more accurate solutions

A Comparison of Three Learning Methods to Predict N2O Fluxes and N Leaching

The environmental costs of intensive farming activities are often under-estimated or not included into the rural development plans, even though they play an important role in addressing future society¿s needs. This paper focus on the use of statistical learning methods to predict the N2O emissions and N leaching under several conservative scenarios, in order to provide an alternative approach to deterministic models at macro-scale. To that aim, three learning methods, namely neural networks (multilayer perceptrons), SVM and random forests, are compared and provide accurate solutions.JRC.DDG.H.2-Climate chang

Fragility curves for Italian residential masonry buildings with retrofit interventions

The earthquakes of the last decades have shown that the Italian residential masonry built her-itage has high seismic vulnerability, in particular when considering structures built before 1919. For this reason, it is necessary to develop effective large-scale risk mitigation strategies in order to reduce the huge losses that could occur in the aftermath of an earthquake. In this paper some retrofit interventions applicable mainly to old buildings are presented, explaining their advantages and potential. These interventions are then implemented, through Vulnus 4.0 software, on a database of 205 buildings built before 1919, previously analyzed in their as-built state. Fragility curves are then developed for each building, and are processed in order to create a vulnerability model for different construction periods that takes into account the possible ret-rofit intervention strategies. Therefore, this procedure allows a comparison between pre and post retrofit intervention fragility, and the results in terms of curves can be used for large scale damage and risk simulations

Tropical deforestation modelling : a comparative analysis of different predictive approaches. The case study of Peten, Guatemala.

The frequent use of predictive models for analysing of complex, natural or artificial, phenomena is changing the traditional approaches to environmental and hazard problems. The continuous improvement of computer performances allows more detailed numerical methods, based on space-time discretisation, to be developed and run for a predictive modeling of complex real systems, reproducing the way their spatial patterns evolve and pointing out the degree of simulation accuracy. In this contribution we present an application of several models (Geomatics, Neural Networks, Land Cover Modeler and Dinamica EGO) in a tropical training area of Peten, Guatemala. During the last decades this region, included into the Biosphere Maya reserve, has known a fast demographic raise and a subsequent uncontrolled pressure on its own geo-resources; the test area can be divided into several sub-regions characterized by different land use dynamics. Understand and quantify these differences permits a better approximation of real system; moreover we have to consider all the physic, socio-economic parameters which will be of use for represent the complex and sometime at random, human impact. Because of the absence of detailed data for our test area, nearly all information were derived from the image processing of 41 ETM+, TM and SPOT scenes; we pointed out the past environmental dynamics and we built the Input layers for the predictive models. The data from 1998 and 2000 were used during the calibration to simulate the Land Cover changes in 2003, selected as reference date for the validation. The basic statistics permit to highlight the qualities or the weaknesses for each model on the different sub-regions

Sensitivity of the process-based model DNDC on microbiological parameters

International audienceProcess-based model such as DNDC rely on a large numbers of parameters which were defined by the model developer on the basis of existing references. Subsequently, some values have been changed to improve model performance for specific applications, often without adequate documentation. Many of these parameters are thus estimates of the real values appropriate for local conditions introducing approximation errors for applications at larger scales. Spatially explicit datasets might be required for some parameters for which model output is highly sensitive. We will present a sensitivity analysis of 38 mainly micro-biological internal parameter of DNDC-EUROPE

Mechanics-based fragility curves for Italian residential URM buildings

Seismic risk assessment at the territorial level is now widely recognised as essential for countries with intense seismic activity, such as Italy. Academia is called to give its contribution in order to synergically deepen the knowledge about the various components of this risk, starting from the complex evaluation of vulnerability of the built heritage. In line with this, a mechanics-based seismic fragility model for Italian residential masonry buildings was developed and presented in this paper. This model is based on the classification of the building stock in macro-typologies, defined by age of construction and number of storeys, which being information available at national level, allow simulating damage scenarios and carrying out risk analyses on a territorial scale. The model is developed on the fragility of over 500 buildings, sampled according to national representativeness criteria and analysed through the Vulnus_4.0 software. The calculated fragility functions were extended on the basis of a reference model available in the literature, which provides generic fragilities for the EMS98 vulnerability classes, thus obtaining a fragility model defined on the five EMS98 damage states. Lastly, to assess the reliability of the proposed model, this was used to simulate damage scenarios due to the 2009 L’Aquila earthquake. Overall, the comparison between model results and observed damage showed a good fit, proving the model effectiveness

Assessing the impacts of the EU bioeconomy on third countries

To achieve its decarbonisation targets and boost the bioeconomy, the EU will inevitably consume more biomass. The EU’s own biomass resources will meet part of the demand although these ambitious targets will also require reliable and sustained access to third country suppliers. This ex-ante study assesses the potential impacts on land use changes, and associated GHG emissions, in Brazil resulting from increases in EU demand for ethanol to 2030, and draws evidence-based conclusions to verify the compliance of sugarcane feedstock production with the REDII environmental criteria. Land use changes due to expansion of the other main crops, including soybean, have also been calculated. Finally, the study points out that the difference between the country’s Nationally Determined Contribution (NDC) targets by 2030 (ca. 22 million CO2 tons) and our results is approximately an additional 900 million CO2 tons, which could put the country's contribution to Paris Agreement at risk.JRC.D.1-Bio-econom

Modelling Tropical Deforestation: A Comparison of Approaches

International audienceTropical deforestation, as an important factor in global change, is a topic that recently has received considerable attention. GIS-based spatially explicit models that intend to predict the location of land use/cover change (LUCC) can help scientists and policy makers to understand, anticipate and possibly prevent the adverse effects of land-use change. There are many approaches and softwares to model LUCC such as CLUE-S, DINAMICA GEOMOD and IDRISI. This study intends to compare these four modelling approaches. First, a review of methods and tools employed by each software to carry out the simulation was done. Then, the four packages were applied to a "virtual" case which involves a map of deforestation, which comprises two types of deforestation (forest to shifting agriculture and forest to pasture lands), along with several explanatory variables (drivers). Deforestation was modelled using the four approaches and the output maps were compared