The rationales of resilience in English and Dutch flood risk policies

We compared the governance of flood risk in England and the Netherlands, focusing on the general policies, instruments used and underlying principles. Both physical and political environments are important in explaining how countries evolved towards very different rationales of resilience. Answering questions as ‘who decides’, ‘who should act’ and ‘who is responsible and liable for flood damage’ systematically, results in a quite fundamental difference in what resilience means, and how this affects the governance regime. In the Netherlands, there is nationwide collective regime with a technocracy based on the merit of water expertise, legitimated by a social contract of government being responsible and the general public accepting and supporting this. In England there also is a technocracy, but this is part of a general-political and economic-rational decision-making process, with responsibilities spread over state, insurance companies, individuals and communities. The rationales are connected to specific conceptions of the public interest, leading to specific governance principles. In both countries, flood risk strategies are discussed in the light of climate change effects, but resilience strategies show more persistence, although combined with gradual adaptation of practices on lower scales, than great transformations

Is flood defense changing in nature? Shifts in the flood defense strategy in six European countries

In many countries, flood defense has historically formed the core of flood risk management but this strategy is now evolving with the changing approach to risk management. This paper focuses on the neglected analysis of institutional changes within the flood defense strategies formulated and implemented in six European countries (Belgium, England, France, the Netherlands, Poland, and Sweden). The evolutions within the defense strategy over the last 30 years have been analyzed with the help of three mainstream institutional theories: a policy dynamics-oriented framework, a structure-oriented institutional theory on path dependency, and a policy actors-oriented analysis called the advocacy coalitions framework. We characterize the stability and evolution of the trends that affect the defense strategy in the six countries through four dimensions of a policy arrangement approach: actors, rules, resources, and discourses. We ask whether the strategy itself is changing radically, i.e., toward a discontinuous situation, and whether the processes of change are more incremental or radical. Our findings indicate that in the European countries studied, the position of defense strategy is continuous, as the classical role of flood defense remains dominant. With changing approaches to risk, integrated risk management, climate change, urban growth, participation in governance, and socioeconomic challenges, the flood defense strategy is increasingly under pressure to change. However, these changes can be defined as part of an adaptation of the defense strategy rather than as a real change in the nature of flood risk management

Toward more flood resilience: is a diversification of flood risk management strategies the way forward?

European countries face increasing flood risks due to urbanization, increase of exposure and damage potential, and the effects of climate change. In literature and in practice, it is argued that a diversification of strategies for flood risk management (FRM) - including flood risk prevention (through pro-active spatial planning), flood defense, flood risk mitigation, flood preparation and flood recovery - makes countries more flood resilient. While this thesis is plausible, it should still be empirically scrutinized. This paper aims to do this. Drawing on existing literature we operationalize the notion of "flood resilience" into three capacities: capacity to resist; capacity to absorb and recover; and capacity to transform and adapt. Based on findings from the EU FP7 project STAR-FLOOD, we explore the degree of diversification of FRM strategies and related flood risk governance arrangements at the national level in Belgium, England, France, The Netherlands, Poland and Sweden, as well as these countries' achievement in terms of the three capacities. We found that The Netherlands and to a lesser extent Belgium have a strong "capacity to resist", France a strong "capacity to absorb and recover" and especially England a high capacity to transform and adapt. Having a diverse portfolio of FRM strategies in place may be conducive to high achievements related to the capacities to absorb/recover and to transform and adapt. Hence, we conclude that diversification of FRM strategies contributes to resilience. However, the diversification thesis should be nuanced in the sense that there are different ways to be resilient. First, the three capacities imply different rationales and normative starting points for flood risk governance, the choice between which is inherently political. Second, we found trade-offs between the three capacities, e.g. being resistant seems to lower the possibility to be absorbent. Third, to explain countries' achievements in terms of resilience, the strategies' feasibility in specific physical circumstances and their fit in existing institutional contexts (appropriateness) as well as the establishment of links between strategies, through bridging mechanisms, have also been shown to be crucial factors. The paper provides much needed reflection on the implications of this diagnosis for governments, private parties and citizens who want to increase flood resilience

Performance of neural networks for localizing moving objects with an artificial lateral line

Fish are able to sense water flow velocities relative to their body with their mechanoreceptive lateral line organ. This organ consists of an array of flow detectors distributed along the fish body. Using the excitation of these individual detectors, fish can determine the location of nearby moving objects. Inspired by this sensory modality, it is shown here how neural networks can be used to extract an object's location from simulated excitation patterns, as can be measured along arrays of stationary artificial flow velocity sensors. The applicability, performance and robustness with respect to input noise of different neural network architectures are compared. When trained and tested under high signal to noise conditions (46 dB), the Extreme Learning Machine architecture performs best with a mean Euclidean error of 0.4% of the maximum depth of the field D, which is taken half the length of the sensor array. Under lower signal to noise conditions Echo State Networks, having recurrent connections, enhance the performance while the Multilayer Perceptron is shown to be the most noise robust architecture. Neural network performance decreased when the source moves close to the sensor array or to the sides of the array. For all considered architectures, increasing the number of detectors per array increased localization performance and robustness

Ensemble machine learning prediction and variable importance analysis of 5-year mortality after cardiac valve and CABG operations

Despite having a similar post-operative complication profile, cardiac valve operations are associated with a higher mortality rate compared to coronary artery bypass grafting (CABG) operations. For long-term mortality, few predictors are known. In this study, we applied an ensemble machine learning (ML) algorithm to 88 routinely collected peri-operative variables to predict 5-year mortality after different types of cardiac operations. The Super Learner algorithm was trained using prospectively collected peri-operative data from 8241 patients who underwent cardiac valve, CABG and combined operations. Model performance and calibration were determined for all models, and variable importance analysis was conducted for all peri-operative parameters. Results showed that the predictive accuracy was the highest for solitary mitral (0.846 [95% CI 0.812-0.880]) and solitary aortic (0.838 [0.813-0.864]) valve operations, confirming that ensemble ML using routine data collected perioperatively can predict 5-year mortality after cardiac operations with high accuracy. Additionally, post-operative urea was identified as a novel and strong predictor of mortality for several types of operation, having a seemingly additive effect to better known risk factors such as age and postoperative creatinine.Peer reviewe

Deep Learning for Identification of Acute Illness and Facial Cues of Illness

Background: The inclusion of facial and bodily cues (clinical gestalt) in machine learning (ML) models improves the assessment of patients' health status, as shown in genetic syndromes and acute coronary syndrome. It is unknown if the inclusion of clinical gestalt improves ML-based classification of acutely ill patients. As in previous research in ML analysis of medical images, simulated or augmented data may be used to assess the usability of clinical gestalt. Objective: To assess whether a deep learning algorithm trained on a dataset of simulated and augmented facial photographs reflecting acutely ill patients can distinguish between healthy and LPS-infused, acutely ill individuals. Methods: Photographs from twenty-six volunteers whose facial features were manipulated to resemble a state of acute illness were used to extract features of illness and generate a synthetic dataset of acutely ill photographs, using a neural transfer convolutional neural network (NT-CNN) for data augmentation. Then, four distinct CNNs were trained on different parts of the facial photographs and concatenated into one final, stacked CNN which classified individuals as healthy or acutely ill. Finally, the stacked CNN was validated in an external dataset of volunteers injected with lipopolysaccharide (LPS). Results: In the external validation set, the four individual feature models distinguished acutely ill patients with sensitivities ranging from 10.5% (95% CI, 1.3–33.1% for the skin model) to 89.4% (66.9–98.7%, for the nose model). Specificity ranged from 42.1% (20.3–66.5%) for the nose model and 94.7% (73.9–99.9%) for skin. The stacked model combining all four facial features achieved an area under the receiver characteristic operating curve (AUROC) of 0.67 (0.62–0.71) and distinguished acutely ill patients with a sensitivity of 100% (82.35–100.00%) and specificity of 42.11% (20.25–66.50%). Conclusion: A deep learning algorithm trained on a synthetic, augmented dataset of facial photographs distinguished between healthy and simulated acutely ill individuals, demonstrating that synthetically generated data can be used to develop algorithms for health conditions in which large datasets are difficult to obtain. These results support the potential of facial feature analysis algorithms to support the diagnosis of acute illness

Recurrent governance challenges in the implementation and alignment of flood risk management strategies: a review

In Europe increasing flood risks challenge societies to diversify their Flood Risk Management Strategies (FRMSs). Such a diversification implies that actors not only focus on flood defence, but also and simultaneously on flood risk prevention, mitigation, preparation and recovery. There is much literature on the implementation of specific strategies and measures as well as on flood risk governance more generally. What is lacking, though, is a clear overview of the complex set of governance challenges which may result from a diversification and alignment of FRM strategies. This paper aims to address this knowledge gap. It elaborates on potential processes and mechanisms for coordinating the activities and capacities of actors that are involved on different levels and in different sectors of flood risk governance, both concerning the implementation of individual strategies and the coordination of the overall set of strategies. It identifies eight overall coordination mechanisms that have proven to be useful in this respect

Warm-Start AlphaZero Self-Play Search Enhancements

Recently, AlphaZero has achieved landmark results in deep reinforcement learning, by providing a single self-play architecture that learned three different games at super human level. AlphaZero is a large and complicated system with many parameters, and success requires much compute power and fine-tuning. Reproducing results in other games is a challenge, and many researchers are looking for ways to improve results while reducing computational demands. AlphaZero's design is purely based on self-play and makes no use of labeled expert data ordomain specific enhancements; it is designed to learn from scratch. We propose a novel approach to deal with this cold-start problem by employing simple search enhancements at the beginning phase of self-play training, namely Rollout, Rapid Action Value Estimate (RAVE) and dynamically weighted combinations of these with the neural network, and Rolling Horizon Evolutionary Algorithms (RHEA). Our experiments indicate that most of these enhancements improve the performance of their baseline player in three different (small) board games, with especially RAVE based variants playing strongly