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Modelling Zero-inflated Rainfall Data through the Use of Gaussian Process and Bayesian Regression
(2018)
Rainfall is a key parameter for understanding the water cycle. An accurate rainfall measurement is vital in the development of hydrological models. By means of indirect measurement, satellites can nowadays estimate the rainfall around the world. However, these measurements are not always accurate. As a first approach to generate a bias-corrected rainfall estimate using satellite data, the performance of Gaussian process and Bayesian regression is studied. The results show Gaussian process as the better option for this dataset but leave place to improvements on both modelling strategies.
An essential task for operation and planning of biogas plants is the optimization of substrate feed mixtures. Optimizing the monetary gain requires the determination of the exact amounts of maize, manure, grass silage, and other substrates. Accurate simulation models are mandatory for this optimization, because the underlying chemical processes are very slow. The simulation models themselves may be time-consuming to evaluate, hence we show how to use surrogate-model-based approaches to optimize biogas plants efficiently. In detail, a Kriging surrogate is employed. To improve model quality of this surrogate, we integrate cheaply available data into the optimization process. Doing so, Multi-fidelity modeling methods like Co-Kriging are employed. Furthermore, a two-layered modeling approach is employed to avoid deterioration of model quality due to discontinuities in the search space. At the same time, the cheaply available data is shown to be very useful for initialization of the employed optimization algorithms. Overall, we show how biogas plants can be efficiently modeled using data-driven methods, avoiding discontinuities as well as including cheaply available data. The application of the derived surrogate models to an optimization process is shown to be very difficult, yet successful for a lower problem dimension.
Learning board games by self-play has a long tradition in computational intelligence for games. Based on Tesauro’s seminal success with TD-Gammon in 1994, many successful agents use temporal difference learning today. But in order to be successful with temporal difference learning on game tasks, often a careful selection of features and a large number of training games is necessary. Even for board games of moderate complexity like Connect-4, we found in previous work that a very rich initial feature set and several millions of game plays are required. In this work we investigate different approaches of online-adaptable learning rates like Incremental Delta Bar Delta (IDBD) or Temporal Coherence Learning (TCL) whether they have the potential to speed up learning for such a complex task. We propose a new variant of TCL with geometric step size changes. We compare those algorithms with several other state-of-the-art learning rate adaptation algorithms and perform a case study on the sensitivity with respect to their meta parameters. We show that in this set of learning algorithms those with geometric step size changes outperform those other algorithms with constant step size changes. Algorithms with nonlinear output functions are slightly better than linear ones. Algorithms with geometric step size changes learn faster by a factor of 4 as compared to previously published results on the task Connect-4.
Cyclone separators are popular devices used to filter dust from the emitted flue gases. They are applied as pre-filters in many industrial processes including energy production and grain processing facilities.
Increasing computational power and the availability of 3D printers provide new tools for the combination of modeling and experimentation, which necessary for constructing efficient cyclones. Several simulation tools can be run in parallel, e.g., long running CFD simulations can be accompanied by experiments with 3D printers. Furthermore, results from analytical and data-driven models can be incorporated. There are fundamental differences between these modeling approaches: some models, e.g., analytical models, use domain knowledge, whereas data-driven models do not require any information about the underlying processes.
At the same time, data-driven models require input and output data, whereas analytical models do not. Combining results from models with different input-output structure is of great interest. This combination inspired the development of a new methodology. An optimization via multimodel simulation approach, which combines results from different models, is introduced.
Using cyclonic dust separators (cyclones) as a real-world simulation problem, the feasibility of this approach is demonstrated. Pros and cons of this approach are discussed and experiences from the experiments are presented.
Furthermore, technical problems, which are related to 3D-printing approaches, are discussed.
Increasing computational power and the availability of 3D printers provide new tools for the combination of modeling and experimentation. Several simulation tools can be run independently and in parallel, e.g., long running computational fluid dynamics simulations can be accompanied by experiments with 3D printers. Furthermore, results from analytical and data-driven models can be incorporated. However, there are fundamental differences between these modeling approaches: some models, e.g., analytical models, use domain knowledge, whereas data-driven models do not require any information about the underlying processes.
At the same time, data-driven models require input and output data, but analytical models do not. Combining results from models with different input-output structures might improve and accelerate the optimization process. The optimization via multimodel simulation (OMMS) approach, which is able to combine results from these different models, is introduced in this paper.
Using cyclonic dust separators as a real-world simulation problem, the feasibility of this approach is demonstrated and a proof-of-concept is presented. Cyclones are popular devices used to filter dust from the emitted flue gases. They are applied as pre-filters in many industrial processes including energy production and grain processing facilities. Pros and cons of this multimodel optimization approach are discussed and experiences from experiments are presented.
An important class of black-box optimization problems relies on using simulations to assess the quality of a given candidate solution. Solving such problems can be computationally expensive because each simulation is very time-consuming. We present an approach to mitigate this problem by distinguishing two factors of computational cost: the number of trials and the time needed to execute the trials. Our approach tries to keep down the number of trials by using Bayesian optimization (BO) –known to be sample efficient– and reducing wall-clock times by parallel execution of trials. We compare the performance of four parallelization methods and two model-free alternatives. Each method is evaluated on all 24 objective functions of the Black-Box-Optimization- Benchmarking (BBOB) test suite in their five, ten, and 20-dimensional versions. Additionally, their performance is investigated on six test cases in robot learning. The results show that parallelized BO outperforms the state-of-the-art CMA-ES on the BBOB test functions, especially for higher dimensions. On the robot learning tasks, the differences are less clear, but the data do support parallelized BO as the ‘best guess’, winning on some cases and never losing.
Many black-box optimization problems rely on simulations to evaluate the quality of candidate solutions. These evaluations can be computationally expensive and very time-consuming. We present and approach to mitigate this problem by taking into consideration two factors: The number of evaluations and the execution time. We aim to keep the number of evaluations low by using Bayesian optimization (BO) – known to be sample efficient– and to reduce wall-clock times by executing parallel evaluations. Four parallelization methods using BO as optimizer are compared against the inherently parallel CMA-ES. Each method is evaluated on all the 24 objective functions of the Black-Box-Optimization-Benchmarking test suite in their 20-dimensional versions. The results show that parallelized BO outperforms the state-of-the-art CMA-ES on most of the test functions, also on higher dimensions.
Recovery after extreme events - Lessons learned and remaining challenges in Disaster Risk Reduction
(2017)
Disasters such as the Indian Ocean Tsunami 2004, but also other extreme events such as cyclones, earthquakes and tsunami substantially affect the lives of many thousands of people - they are events radically and abruptly changing local circumstances and needs. At the same time they can significantly reshape global paradigms of Disaster Risk Reduction (DRR). Such events also bring to light the challenges in coordinating assistance from the “global community” with all the intended and un-intended effects. Two of the most pressing questions therefore are whether the different actors have learned from the disaster and whether processes of DRR and livelihood improvements have been implemented successfully.
This volume gathers selected papers addressing the following key questions:
- Lessons learned: Which lessons have been learned in a way that a difference can be seen today for the livelihoods and resilience of local people in the regions affected?
- Lessons to be Learned: Despite the body of knowledge created and reflected in a good number of lessons learned studies – what is still unsolved or needs to be emphasized?
- Monitoring and evaluation: Which DRR measures have been perpetuated and how can they be monitored and evaluated scientifically?
- Resilience effects and (unintended) side-effects: Which coping, recovery and adaptation measures are
supported by the resilience paradigm and which other areas are side-lined, neglected or even contrary to the intended effects?
- Dynamics in risk: In which cases has resilience building taken place? In which cases have ulnerabilities
been shifted internally or new vulnerabilities been created?
- Relocation/resettlement: How did the relocation/resettlement process of displaced people take place and what are its long-term effects?
- Urban-rural divide: How have DRR measures in urban vs. rural areas differed and which linkages but also rifts in rehabilitation and reconstruction initiatives can be observed between the two?
- Early warning: What is the future of Early Warning and how can important top-down information chains benefit from or be balanced with bottom-up feedback of users and affected people?
It appears that extreme disaster events spark a plethora of actions in academia, civil society, media, policy, private sector and other organisations. Tragic, as such disasters are, they offer incentives for learning, locally and globally. Lately, disaster impacts have in many cases been detracted through the application of knowledge and experience gained from previous events. However, there are still a number of challenges with regards to learning from past disasters
Architecural aproaches are considered to simplify the generation of re-usable building blocks in the field of data warehousing. While SAP’s Layer Scalable Architecure (LSA) offers a reference model for creating data warehousing infrastructure based on SAP software, extented reference models are needed to guide the integration of SAP and non-SAP tools. Therefore, SAP’s LSA is compared to the Data Warehouse Architectural Reference Model (DWARM), which aims to cover the classical data warehouse topologies.
A pension system is resilient if it able to absorb external (temporal) shocks and if it is able to adapt to (longterm) shifts of the socio-economic environment. Defined benefit (DB) and defined contribution pension plans behave contrastingly with respect to capital market shocks and shifts: while DB-plan benefits are not affected by external shocks they totally lack adaptability with respect to fundamental changes; DC-plans automatically adjust to a changing environment but any external shock has a direct impact on the (expected) pensions. By adding a collective component to DC-plans one can make these collective DC (CDC)-plans shock absorbing - at least to a certain degree. In our CDC pension model we build a collective reserve of assets that serves as a buffer to capital market shocks, e.g. stock market crashes. The idea is to transfer money from the collective reserve to the individual pension accounts whenever capital markets slump and to feed the collective reserve whenever capital market are booming. This mechanism is particular valuable for age cohorts that are close to retirement. It is clear that withdrawing assets from or adding assets to the collective reserve is essentially a transfer of assets between the age cohorts. In our near reality model we investigate the effect of stock market shocks and interest rate (and mortality) shifts on a CDC- pension system. We are particularly interested in the question, to what extend a CDC-pension system is actually able to absorb shocks and whether the intergenerational transfer of assets via the collective reserve can be regarded as fair.