Toward exploratory analysis of diversity unified across fields of study: an information visualization approach

The study of the diversity of multivariate objects shares common characteristics and goals across disciplines, including ecology and organizational management. Nevertheless, subject-matter experts have adopted somewhat separate diversity concepts and analysis techniques, limiting the potential for sharing and comparing across disciplines. Moreover, while large and complex diversity data may benefit from exploratory data analysis, most of the existing techniques emphasize confirmatory analysis based on statistical metrics and models. This work aims to bridge these gaps. First, by cross comparing the analyses of species diversity, microbial diversity, and workgroup diversity, we introduce a framework of diversity concerns aligned across the three areas. The alignment framework is validated and refined by feedback from subject-matter experts. Then, guided by the framework and theoretical information visualization and visual analytics principles (as distinguished from scientific visualization), we propose a unified taxonomy of common analytical tasks for exploration of diversity.     

Understanding spatial variations of malaria in Vietnam using remotely sensed data integrated into GIS and machine learning classifiers

Abstract

This study examines the potentials of remotely sensed data, GIS and some machine learning classifiers and ensemble techniques in the investigation of the non-linear relationship between malaria occurrences and socio-physical conditions in the Dak Nong province of Viet Nam. Accuracy assessment was determined with Receiver Operating Characteristic (ROC) curve and pair t-test. The results showed that the area under ROC of Random Subspace ensemble model performed better than the other models based on statistical indicators. Comparing pair t-test with Area Under Curve values showed a slight difference of about 1%. Therefore ensemble techniques had significantly improved the performance of the base classifier. However, the performances might vary according to geographic locations. It is concluded that the machine learning classifiers combined with remotely sensed data and GIS is promising for malaria vulnerability mapping, and the derived maps can be used as a fundamental basis for programmes on spatial disease control.     

Public perception of climate change and disaster preparedness: Evidence from the Philippines

The Philippines is highly susceptible to both geophysical and climate-related disasters. This article explores Filipinos knowledge and perception of climate change and their association with what action Filipinos take to prepare for rapid onset natural hazards such as typhoons. Data for this study were collected from a nationally representative random survey of 5,184 adults conducted between March and April of 2017. Filipinos self-report relatively low levels of knowledge of climate change and cited increased temperatures, shifts in seasons, and heavier rains as the most likely consequences. Levels of disaster preparedness in the Philippines differ widely by region. Although most Filipinos perceive that natural hazards are a risk to them, only a third of Filipinos undertake measures to prepare for disasters. Filipinos who perceive climate-related changes directly impacting their households report taking greater action to prepare for disasters. Filipinos who believe they have been directly impacted by climate-related changes are also more likely to prepare for disasters, take planning actions, and undertake material actions to prepare, such as dwelling improvements. Other factors associated with disaster preparedness include gender, membership in an association, wealth, risk perception, and prior exposure to and losses due to disasters. The findings imply that, while posing different challenges and requiring different responses, adaptation to climate change and disaster preparedness are inherently associated and potentially mutually reinforcing. Policies and programs would arguably benefit from a more unified intervention framework that links climate change adaptation and disaster preparedness.     

Sensitivity of seismically isolated structures

In this paper we study the sensitivity of seismically isolated structures to a small variability of the earthquake excitation and of some structural properties with respect to the probability of failure and floor spectra. In particular, the influence of the nonlinear behaviour of the isolated superstructure on the vulnerability and on the floor spectra is investigated by means of a series of Monte Carlo simulations of simple two degrees‐of‐freedom systems. Several types of passive and active isolation systems are examined and three different idealized nonlinear constitutive laws are considered for the superstructure. It is found that, in general, the probability of failure does not depend on the specific cyclic behaviour of the assumed constitutive law and general trends regarding the impact of different isolation devices on vulnerability are established. As for the floor spectra, the influence of moderate nonlinear behaviour of isolated superstructures, with the exception of the case of a non‐dissipative elastic nonlinear law, is negligible, contrary to the case of conventional structures. Copyright © 2008 John Wiley & Sons, Ltd.     

An Application of the Multi-Physics Ensemble Kalman Filter to Typhoon Forecast

This study examines the roles of the multi-physics approach in accounting for model errors for typhoon forecasts with the local ensemble transform Kalman filter (LETKF). Experiments with forecasts of Typhoon Conson (2010) using the weather research and forecasting (WRF) model show that use of the WRF’s multiple physical parameterization schemes to represent the model uncertainties can help the LETKF provide better forecasts of Typhoon Conson in terms of the forecast errors, the ensemble spread, the root mean square errors, the cross-correlation between mass and wind field as well as the coherent structure of the ensemble spread along the storm center. Sensitivity experiments with the WRF model show that the optimum number of the multi-physics ensemble is roughly equal to the number of combinations of different physics schemes assigned in the multi-physics ensemble. Additional idealized experiments with the Lorenz 40-variable model to isolate the dual roles of the multi-physics ensemble in correcting model errors and expanding the local ensemble space show that the multi-physics approach appears to be more essential in augmenting the local rank representation of the LETKF algorithm rather than directly accounting for model errors during the early cycles. The results in this study suggest that the multi-physics approach is a good option for short-range forecast applications with full physics models in which the spinup of the ensemble Kalman filter may take too long for the ensemble spread to capture efficiently model errors and cross-correlations among model variables.     

Spatiotemporal variations of extreme sea levels around the South China Sea: assessing the influence of tropical cyclones,monsoons and major climate modes

With sea levels projected to rise as a result of climate change, it is imperative to understand not only long-term average trends, but also the spatial and temporal patterns of extreme sea level. In this study, we use a comprehensive set of 30 tide gauges spanning 1954–2014 to characterize the spatial and temporal variations of extreme sea level around the low-lying and densely populated margins of the South China Sea. We also explore the long-term evolution of extreme sea level by applying a dynamic linear model for the generalized extreme value distribution (DLM-GEV), which can be used for assessing the changes in extreme sea levels with time. Our results show that the sea-level maxima distributions range from ~?90 to 400 cm and occur seasonally across the South China Sea. In general, the sea-level maxima at northern tide gauges are approximately 25–30% higher than those in the south and are highest in summer as tropical cyclone-induced surges dominate the northern signal. In contrast, the smaller signal in the south is dominated by monsoonal winds in the winter. The trends of extreme high percentiles of sea-level values are broadly consistent with the changes in mean sea level. The DLM-GEV model characterizes the interannual variability of extreme sea level, and hence, the 50-year return levels at most tide gauges. We find small but statistically significant correlations between extreme sea level and both the Pacific Decadal Oscillation and El Niño/Southern Oscillation. Our study provides new insight into the dynamic relationships between extreme sea level, mean sea level and the tidal cycle in the South China Sea, which can contribute to preparing for coastal risks at multi-decadal timescales.

     

Landslide Susceptibility Assessment Using Bagging Ensemble Based Alternating Decision Trees,Logistic Regression and J48 Decision Trees Methods: A Comparative Study

In this study, we have evaluated and compared prediction capability of Bagging Ensemble Based Alternating Decision Trees (BADT), Logistic Regression (LR), and J48 Decision Trees (J48DT) for landslide susceptibility mapping at part of the Uttarakhand State (India). The BADT method has been proposed in the present study which is a novel hybrid machine learning ensemble approach of bagging ensemble and alternating decision trees. The J48DT is a relative new machine learning technique which has been applied only in few landslide studies, and the LR is known as a popular landslide susceptibility model. For the model studies, a spatial database of 930 historical landslide events and 15 landslide affecting factors have been collected and analyzed. This database has been used to build and validate the landslide models namely BADT, LR and J48DT Predictive capability of these models has been validated and compared using statistical analyzing methods and Receiver Operating Characteristic (ROC) curve. Results show that these three landslide models (BADT, LR and J48DT) performed well with the training dataset. However, using the validation dataset the BADT model has the highest prediction capability, followed by the LR model, and the J48DT model, respectively. This indicates that the BADT is a promising method which can be used for landslide susceptibility assessment also for other landslide prone areas.     

Sandwaves on the Southeast Vietnam Shelf recorded by high resolution seismic profiles: formation and mechanism

The application of high resolution seismic data using boomer sound source has revealed a wide distribution of large-scale bedforms (sandwaves) on the Southeast Vietnam continental shelf. Bedforms that are a few meters high in wave height and hundreds of meters long in wavelength are primarily developed in the inner shelf (20–40 m) and considered to be formed under the present-day marine hydrodynamic conditions. Those bedforms developed in the deeper water (120 m) of the northernmost part of the continent can be interpreted as the relict morphological features formed during the latest sea-level lowstand of the late Pleistocene period. Two sediment transport paths have been identified on the basis of the bedform’s leeward orientation: northeast-southwest (along-shore) and north-south (cross-shore). A quantitative bottom current map is constructed from sandwave dimensions, surface sediments and measurement data. The strongest current velocities that gradually decrease toward the southwest are indicated by large sandwaves in the north (field B). Water depth, surficial sediment composition and bottom current are three factors that control the development of bedforms.