Conceptual model for quantification of snow avalanche return periods

This note is a contribution to the InfraRisk project (Module B) where one of the aims is to suggest an improved standard for hazard mapping. In particular, it is attempted to overcome the deficieney pointed out for *WP Bl Hazard Mapping” stating that today"s hazard maps are inappropriate to assess the risk inside the hazard zone (the maps define only the frequency of an expected event and most often only along a single line). The aim of this study is to establish a conceptual model for quantification of snow avalanche return periods at any given location along the avalanche track. The conceptual model is based on the modified a/B-model presented by Harbitz et al. (2001), which is again based on the original topographical/statistical /B-model (e.g. Bakkehøi et al. 1983; a summary description is also presented by Harbitz 1998).Norges Forskningsråd (NFR

Parameter-sparse modification of Fourier methods to analyse the shape of closed contours with application to otolith outlines

-Elliptical Fourier descriptors (EFDs) have been used extensively in shape analysis of closed contours and have a range of marine applications, such as automatic identification of fish species and discrimination between fish stocks based on EFDs of otolith contours. A recent method (the ‘MIRR’ method) transforms the two-dimensional contour to a one-dimensional function by mirroring (reflecting) the lower half of the contour around a vertical axis at the right end of the contour. MIRR then applies the fast Fourier transform (FFT) to the vertical contour points corresponding to equidistant coordinate values along the horizontal axis. MIRR has the advantage of reducing the number of Fourier coefficients to two coefficients per frequency component compared with four EFDs. However, both Fourier methods require several frequency components to reproduce a pure ellipse properly. This paper shows how the methods can be easily modified so that a virtually perfect reproduction of a pure ellipse is obtained with only one frequency component. In addition, real otolith examples for cod (Gadus morhua) and Greenland halibut (Reinhardtius hippoglossoides) are used to demonstrate that the modified methods give better approximations to the large-scale shape of the original contour with fewer coefficients than the traditional Fourier methods, with negligible additional computing time

A zigzag survey design for continuous transect sampling with guaranteed equal coverage probability

Marine resource surveys in large areas have high cost, and to find an optimal survey design with regard to efficiency and scientific outcome is an important issue. A randomized zigzag design for straight line and curved transects is developed that guarantees equal coverage probability, i.e., each point in the study area has the same probability of being covered by the transect. The basic idea is to fit automatically either the smallest rectangle, or the smallest circular sector enclosing the actual area. Then a recipe for the location of zigzag legs that provide equal coverage probability everywhere in the rectangle or circular sector is outlined, and thereby also at any location within the study area, which simplifies unbiased abundance estimation. The cost of this approach is the unwanted distance to be traveled from the point where a transect leg leaves the study area to the point where the next leg enters. A comparison of a randomized parallel, straight line zigzag, and curved zigzag approach applied to 7 sandeel areas with great variety revealed an average off-effort traveling distance of 28%, 9% and 6%, respectively. Thus, it appears that the developed zigzag design is far more efficient than the parallel design.publishedVersio

Significance of historical records for avalanche hazard zoning in Norway

In avalanche hazard zoning, it is common practice to investigate the previous avalanche history for the area considered. Historical observations of avalanches serve as an aid in the classification of the terrain, and may also serve as verification of estimates of avalanche runout. Conditions influencing the avalanche occurrences may change significantly over time and it is important to take these changes into account when using historical avalanche observations in hazard ioning today. A number of the most extensive avalanches recorded in Norway, are found during the eigthteenth and nineteenth century. The catastrophes may be linked to weather as well as to socio-econornic conditions, in particular deforestation of mountain slopes. The implications of using or disregarding historical avalanche observation are shown in an example of statistical estimation of avalanche runout

On probability analysis in snow avalanche hazard zoning

The reduced societal acceptance of living in regions exposed to snow avalanches, and the increased economic consequences when houses are located within a hazard zone, highlight the uncertainty concerning avalanche run-out prediction. The limitations of today’s zoning procedures are especially pronounced in potential avalanche terrain where there are few observations of snow avalanches, where old buildings are present in the potential run-out zone, and where the local climate does not favour severe snow accumulation. This paper combines a mechanical probabilistic model for avalanche release with a statistical/topographical model for avalanche run-out distance to obtain the unconditional probability of extreme run-out distance. For the mechanical model, a first-order reliability method (FORM) and Monte Carlo simulations are compared. The interpretation of the statistical/topographical model either as an extreme value model or as a single value model is discussed. Furthermore, both a classical approach where the probability of an avalanche occurring is a constant, and a Bayesian approach with stochastic probability, are compared. Finally, example applications in hazard zoning are presented, with emphasis on how the influence of historical observations, local climate, etc., on run-out distance can be quantified in statistical terms and how a specified certainty level can be found from constructing confidence intervals for, for example, the most likely largest run-out distance during various time intervals

Age prediction by deep learning applied to Greenland halibut (Reinhardtius hippoglossoides) otolith images

Otoliths (ear-stones) in the inner ears of vertebrates containing visible year zones are used extensively to determine fish age. Analysis of otoliths is a time-consuming and difficult task that requires the education of human experts. Human age estimates are inconsistent, as several readings by the same human expert might result in different ages assigned to the same otolith, in addition to an inherent bias between readers. To improve efficiency and resolve inconsistent results in the age reading from otolith images by human experts, an automated procedure based on convolutional neural networks (CNNs), a class of deep learning models suitable for image processing, is investigated. We applied CNNs that perform image regression to estimate the age of Greenland halibut (Reinhardtius hippoglossoides) with good results for individual ages as well as the overall age distribution, with an average CV of about 10% relative to the read ages by experts. In addition, the density distribution of predicted ages resembles the density distribution of the ground truth. By using k*l-fold cross-validation, we test all available samples, and we show that the results are rather sensitive to the choice of test set. Finally, we apply explanation techniques to analyze the decision process of deep learning models. In particular, we produce heatmaps indicating which input features that are the most important in the computation of predicted age.publishedVersio

Automatic Fish Age Determination across Different Otolith Image Labs Using Domain Adaptation

The age determination of fish is fundamental to marine resource management. This task is commonly done by analysis of otoliths performed manually by human experts. Otolith images from Greenland halibut acquired by the Institute of Marine Research (Norway) were recently used to train a convolutional neural network (CNN) for automatically predicting fish age, opening the way for requiring less human effort and availability of expertise by means of deep learning (DL). In this study, we demonstrate that applying a CNN model trained on images from one lab (in Norway) does not lead to a suitable performance when predicting fish ages from otolith images from another lab (in Iceland) for the same species. This is due to a problem known as dataset shift, where the source data, i.e., the dataset the model was trained on have different characteristics from the dataset at test stage, here denoted as target data. We further demonstrate that we can handle this problem by using domain adaptation, such that an existing model trained in the source domain is adapted to perform well in the target domain, without requiring extra annotation effort. We investigate four different approaches: (i) simple adaptation via image standardization, (ii) adversarial generative adaptation, (iii) adversarial discriminative adaptation and (iv) self-supervised adaptation. The results show that the performance varies substantially between the methods, with adversarial discriminative and self-supervised adaptations being the best approaches. Without using a domain adaptation approach, the root mean squared error (RMSE) and coefficient of variation (CV) on the Icelandic dataset are as high as 5.12 years and 28.6%, respectively, whereas by using the self-supervised domain adaptation, the RMSE and CV are reduced to 1.94 years and 11.1%. We conclude that careful consideration must be given before DL-based predictors are applied to perform large scale inference. Despite that, domain adaptation is a promising solution for handling problems of dataset shift across image labs.publishedVersio

Parameter-sparse modification of Fourier methods to analyse the shape of closed contours with application to otolith outlines

-Elliptical Fourier descriptors (EFDs) have been used extensively in shape analysis of closed contours and have a range of marine applications, such as automatic identification of fish species and discrimination between fish stocks based on EFDs of otolith contours. A recent method (the ‘MIRR’ method) transforms the two-dimensional contour to a one-dimensional function by mirroring (reflecting) the lower half of the contour around a vertical axis at the right end of the contour. MIRR then applies the fast Fourier transform (FFT) to the vertical contour points corresponding to equidistant coordinate values along the horizontal axis. MIRR has the advantage of reducing the number of Fourier coefficients to two coefficients per frequency component compared with four EFDs. However, both Fourier methods require several frequency components to reproduce a pure ellipse properly. This paper shows how the methods can be easily modified so that a virtually perfect reproduction of a pure ellipse is obtained with only one frequency component. In addition, real otolith examples for cod (Gadus morhua) and Greenland halibut (Reinhardtius hippoglossoides) are used to demonstrate that the modified methods give better approximations to the large-scale shape of the original contour with fewer coefficients than the traditional Fourier methods, with negligible additional computing time

Workshop on hydro-acoustics scrutinizing in the Norwegian Sea

This report presents the international redfish survey carried out in the Norwegian Sea in August 2008 and the methodology used to review and compare the different hydroacoustic scrutinizing procedures. The results of the comparative analysis clearly show that differences in scrutinizing methods have a very large impact on the abundance estimate of redfish. They probably constitute the major source of uncertainty for any quantitative estimate. Efforts towards standardisation of scrutinizing procedures should be amplified or at least maintained

Advice on fishing opportunities for Barents Sea capelin in 2024 — ICES subareas 1 and 2 excluding Division 2.a west of 5°W

publishedVersio