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Sequential Parameter Optimization is a model-based optimization methodology, which includes several techniques for handling uncertainty. Simple approaches such as sharp- ening and more sophisticated approaches such as optimal computing budget allocation are available. For many real world engineering problems, the objective function can be evaluated at different levels of fidelity. For instance, a CFD simulation might provide a very time consuming but accurate way to estimate the quality of a solution.The same solution could be evaluated based on simplified mathematical equations, leading to a cheaper but less accurate estimate. Combining these different levels of fidelity in a model-based optimization process is referred to as multi-fidelity optimization. This chapter describes uncertainty-handling techniques for meta-model based search heuristics in combination with multi-fidelity optimization. Co-Kriging is one power- ful method to correlate multiple sets of data from different levels of fidelity. For the first time, Sequential Parameter Optimization with co-Kriging is applied to noisy test functions. This study will introduce these techniques and discuss how they can be applied to real-world examples.
Land-use intensification and urbanisation processes are degrading ecosystem services in the Guapiaçu-Macacu watershed in the state of Rio de Janeiro, Brazil. Paying farmers to forgo agricultural production activities in order to restore natural watershed services might be a viable means of securing water resources over the long term for the approximately 2.5 million urban water users in the region. This study quantified the costs of changing current land-use patterns to enhance watershed services. These costs are compared to estimates of the avoided water treatment costs for the public potable water supply as a proxy of willingness-to-pay for watershed services. Farm-household data was used to estimate the opportunity costs of abandoning current land uses in order to allow natural vegetation succession; a process that is very likely to improve water quality in terms of reducing erosion and subsequently water turbidity. Opportunity cost estimates were extrapolated to the watershed scale based on land-use classifications and a vulnerability analysis for identifying priority areas for watershed management interventions. Water quality and treatment cost data from the primary local water treatment plant (principal water user in the study area) were analysed to assess the potential demand for watershed services. The conversion of agricultural land uses for the benefit of watershed service provision was found to entail high opportunity costs in the study area, which is near the city of Rio de Janeiro. Alternative, relatively low-cost practices that support watershed conservation do exist for the livestock production systems. Other options include: implementing soil conservation techniques, permanent protection of areas that are vulnerable to erosion, protecting and restoring riparian and headwater areas, and applying more sustainable agricultural practices. These measures have the potential to directly reduce the amount of sediment and nutrients reaching water bodies and, in turn, decrease the costs of treatment required for providing the potable water supply. Based on treatment costs, the state water utility company’s willingness-to-pay for watershed services alone will not be sufficient to compensate farmers for forgoing agricultural production activities in order to improve the provision of additional watershed services. The results suggest that the opportunity costs of land-cover changes at the scale needed to improve water quality will likely exceed the cost of additional investments in water treatment. Monetary incentives conditioned on specific adjustments to existing production systems could offer a complementary role for improving watershed services. The willingness-to-pay analysis, however, only focused on chemical treatment costs and one of a potentially wide range of ecosystem services provided by the natural vegetation in the Guapiaçu-Macacu watershed (water quality maintenance for potable water provision). Other ecosystem services provided by forest cover include carbon sequestration and storage, moderation of extreme weather events, regulation of water flows, landscape aesthetics, and biodiversity protection. Factoring these additional ecosystem services into the willingness-to-pay equation is likely to change the conclusions of the assessment in favour of additional conservation action, either through payments for ecosystem services (PES) or other policy instruments. This effort contributes to the growing body of related scientific literature by offering additional knowledge on how to combine spatially explicit economic and environmental information to provide valuable insights into the feasibility of implementing PES schemes at the scale of entire watersheds. This is relevant to helping inform decision-making processes with respect to the economic scope of incentive-based watershed management in the context of the Guapiaçu-Macacu watershed. Furthermore, the findings of this research can serve long-term watershed conservation initiatives and public policy in other watersheds of the Atlantic Forest biome by facilitating the targeting of conservation incentives for a cost-effective watershed management.
This volume addresses the topics of Disaster Risk Reduction (DRR) and Climate Change Adaption (CCA) from the perspective of participants of the DAAD Alumni Summer School 2013. Contributions from 16 countries are gathered in this volume, thereby sharing specific knowledge on climate extremes, disasters, adaptation and prevention measures as well as current strategies in a range of different national contexts. The DAAD Alumni Summer School opened up a forum for integrative thinking and learning across cultures, disciplines and institutions. This volume is directly linked to the first volume of the series and presents a further outcome of the Summer School 2013. It invites the reader to look beyond common perspectives of DRR and CCA and relates climate change and natural disasters with interdisciplinary and bottom-up policy making. The outcomes presented in the two volumes are a starting point for further international & transdisciplinary knowledge exchange activities planned for the upcoming years.
Computational intelligence methods have gained importance in several real-world domains such as process optimization, system identification, data mining, or statistical quality control. Tools are missing, which determine the applicability of computational intelligence methods in these application domains in an objective manner. Statistics provide methods for comparing algorithms on certain data sets. In the past, several test suites were presented and considered as state of the art. However, there are several drawbacks of these test suites, namely: (i) problem instances are somehow artificial and have no direct link to real-world settings; (ii) since there is a fixed number of test instances, algorithms can be fitted or tuned to this specific and very limited set of test functions; (iii) statistical tools for comparisons of several algorithms on several test problem instances are relatively complex and not easily to analyze. We propose amethodology to overcome these dificulties. It is based on standard ideas from statistics: analysis of variance and its extension to mixed models. This work combines essential ideas from two approaches: problem generation and statistical analysis of computer experiments.
This paper proposes an experimental methodology for on-line machine learning algorithms, i.e., for algorithms that work on data that are available in a sequential order.
It is demonstrated how established tools from experimental algorithmics (EA) can be applied in the on-line or streaming data setting.
The massive on-line analysis (MOA) framework is used to perform the experiments.
Benefits of a well-defined report structure are discussed.
The application of methods from the EA community to on-line or streaming data is referred to as experimental algorithmics for streaming data (EADS).
In this paper we present a comparison of different data driven modeling methods. The first instance of a data driven linear Bayesian model is compared with several linear regression models, a Kriging model and a genetic programming model.
The models are build on industrial data for the development of a robust gas sensor.
The data contain limited amount of samples and a high variance.
The mean square error of the models implemented in a test dataset is used as the comparison strategy.
The results indicate that standard linear regression approaches as well as Kriging and GP show good results,
whereas the Bayesian approach, despite the fact that it requires additional resources, does not lead to improved results.
When using machine learning techniques for learning a function approximation from given data it is often a difficult task to select the right modeling technique.
In many real-world settings is no preliminary knowledge about the objective function available. Then it might be beneficial if the algorithm could learn all models by itself and select the model that suits best to the problem.
This approach is known as automated model selection. In this work we propose a
generalization of this approach.
It combines the predictions of several into one more accurate ensemble surrogate model. This approach is studied in a fundamental way, by first evaluating minimalistic ensembles of only two surrogate models in detail and then proceeding to ensembles with three and more surrogate models.
The results show to what extent combinations of models can perform better than single surrogate models and provides insights into the scalability and robustness of the approach. The study focuses on multi-modal functions topologies, which are important in surrogate-assisted global optimization.
This report presents a practical approach to stacked generalization in surrogate model based optimization. It exemplifies the integration of stacking methods into the surrogate model building process. First, a brief overview of the current state in surrogate model based opti- mization is presented. Stacked generalization is introduced as a promising ensemble surrogate modeling approach. Then two examples (the first is based on a real world application and the second on a set of artificial test functions) are presented. These examples clearly illustrate two properties of stacked generalization: (i) combining information from two poor performing models can result in a good performing model and (ii) even if the ensemble contains a good performing model, combining its information with information from poor performing models results in a relatively small performance decrease only.
When researchers and practitioners in the field of
computational intelligence are confronted with real-world
problems, the question arises which method is the best to
apply. Nowadays, there are several, well established test
suites and well known artificial benchmark functions
available.
However, relevance and applicability of these methods to
real-world problems remains an open question in many
situations. Furthermore, the generalizability of these
methods cannot be taken for granted.
This paper describes a data-driven approach for the
generation of test instances, which is based on
real-world data. The test instance generation uses
data-preprocessing, feature extraction, modeling, and
parameterization. We apply this methodology on a classical
design of experiment real-world project and generate test
instances for benchmarking, e.g. design methods, surrogate
techniques, and optimization algorithms. While most
available results of methods applied on real-world
problems lack availability of the data for comparison,
our future goal is to create a toolbox covering multiple
data sets of real-world projects to provide a test
function generator to the research community.
Data pre-processing is a key research topic in data mining because it plays a
crucial role in improving the accuracy of any data mining algorithm. In most
real world cases, a significant amount of the recorded data is found missing
due to most diverse errors. This loss of data is nearly always unavoidable.
Recovery of missing data plays a vital role in avoiding inaccurate data
mining decisions. Most multivariate imputation methods are not compatible
to univariate datasets and the traditional univariate imputation techniques
become highly biased as the missing data gap increases. With the current
technological advancements abundant data is being captured every second.
Hence, we intend to develop a new algorithm that enables maximum
utilization of the available big datasets for imputation. In this paper, we
present a Seasonal and Trend decomposition using Loess (STL) based
Seasonal Moving Window Algorithm, which is capable of handling patterns
with trend as well as cyclic characteristics. We show that the algorithm is
highly suitable for pre-processing of large datasets.