Indices for the evaluation of wildfire spread simulations using contemporaneous predictions and observations of burnt area

Methods to objectively evaluate performance are critical for model development. In contrast to recent advances in wildfire simulation, there has been limited attention to evaluating fire model performance. Information to validate fire models is typically limited, commonly to a few perimeter observations at a small number of points in time. We review metrics for comparing two burnt areas at a point in time: observed and predicted. These are compared in an idealised landscape and with a case study evaluating the performance of simulations of an Australian wildfire. We assessed: Shape Deviation Index (SDI), Jaccard's coefficient, F1, Sørensen's Similarity and Area Difference Index (ADI). For decomposing fit into error components (overprediction and underprediction) we assessed the partial indices of SDI and ADI, Precision and Recall. The various metrics were evaluated for their ability to represent error and their suitability for use in model improvement frameworks.     

A web based DSS for the management of floods and wildfires (FLIRE) in urban and periurban areas

The FLIRE DSS is a web-based Decision Support System for the combined forest and flood risk management and planning. State of the art tools and models have been used in order to enable Civil Protection agencies and local stakeholders to take advantage of web based DSS with no need of local complex infrastructure and maintenance. Civil protection agencies can predict the behavior of a fire event using real time data and in that way to plan its efficient elimination. Also, they can implement “what-if” scenarios for areas prone to fire and thus develop plans for forest fire management. Flood services include flood maps and flood-related warnings; these become available to relevant authorities for visualization and further analysis on a daily basis. Real time weather data from ground stations provide the necessary inputs for the calculation of the fire model in real time and a high resolution weather forecast grid support flood modeling and “what-if” scenarios for the fire modeling. The innovations of the FLIRE DSS are the use of common Earth Observation (EO) data as the backbone of the system to produce data for the support of fire and flood models, the common use of weather related information, the distributed architecture of the system and the web-based access of it with no need for installation of dedicated software. All these can be accessed by all means of computer sources like PC, laptop, Smartphone and tablet either by normal network connection or by using 3G and 4G cellular network. The latter is important for the accessibility of the FLIRE DSS during firefighting or rescue operations during flood events. FLIRE DSS can be easily transferred to other areas with similar characteristics due to its robust architecture and its flexibility.     

An unsupervised learning method with a clustering approach for tumor identification and tissue segmentation in magnetic resonance brain images

Malignant and benign types of tumor infiltrated in human brain are diagnosed with the help of an MRI scanner. With the slice images obtained using an MRI scanner, certain image processing techniques are utilized to have a clear anatomy of brain tissues. One such image processing technique is hybrid self-organizing map (SOM) with fuzzy K means (FKM) algorithm, which offers successful identification of tumor and good segmentation of tissue regions present inside the tissues of brain. The proposed algorithm is efficient in terms of Jaccard Index, Dice Overlap Index (DOI), sensitivity, specificity, peak signal to noise ratio (PSNR), mean square error (MSE), computational time and memory requirement. The algorithm proposed through this paper has better data handling capacities and it also performs efficient processing upon the input magnetic resonance (MR) brain images. Automatic detection of tumor region in MR (magnetic resonance) brain images has a high impact in helping the radio surgeons assess the size of the tumor present inside the tissues of brain and it also supports in identifying the exact topographical location of tumor region. The proposed hybrid SOM-FKM algorithm assists the radio surgeon by providing an automated tissue segmentation and tumor identification, thus enhancing radio therapeutic procedures. The efficiency of the proposed technique is verified using the clinical images obtained from four patients, along with the images taken from Harvard Brain Repository.     

河库联合调度预报技术探析

贺江流域"2013.8"暴雨洪水期间,通过准确入库洪水预报,以贺江下游重要城镇安全泄洪量为控制,科学合理利用合面狮水库成功进行调蓄错峰,取得良好的防洪效果,分析了开展河库联合调度预报的技术路线及方法,为广泛开展河库联合调度预报服务提供参考及借鉴。     

梧州市平浪防洪堤总体设计方案选择

介绍了梧州市平浪防洪堤堤线、堤型的选择原则、思路,总体设计方案等基本情况。该设计方案融入可持续发展及生态水利的理念,将环保、人文以及可持续发展等贯彻于设计的全过程,体现了人与自然的和谐共处。     

Mapping and classification of direct flood impacts in the complex conditions of an urban environment. The case study of the 2014 flood in Athens,Greece

Urban flooding is a gradually increasing problem as the urban population expands into floodplains. In urban environments, flood vulnerability is significantly increased as a more concentrated population and assets makes flooding costly and challenging, in terms of impact estimation. This work focuses on mapping and classifying impacts after the catastrophic 2014 flood in Athens, Greece. The study proposes a method for classifying flood effects into four categories including: the natural and built environment, mobile objects and human population, organized in five classes of increasing severity, i.e. minor, weak, moderate, strong and extreme. Flood effects are grouped based on the qualitative nature of the recorded effects, allowing the development of an impact-severity map. Mapping of the 2014 flood effects indicated specific locations where the severity of impacts was distinctively higher than others, providing a holistic overview of the flood’s effects and highlighting the usefulness of the approach in future flood protection planning.     

Optimization of a spectrally tunable LED daylight simulator

Daylight simulators (i.e., D50, D55, D65, and D75 simulators) play an important role for industrial applications, such as surface color industry, recipe formulation, and graphic arts industry. Conventional light sources, including xenon lamps, tungsten‐halogen lamps, and fluorescent lamps, have been used as daylight simulators. In this article, a genetic algorithm is employed to investigate optimized channels for each daylight simulator. It is found that at least six LED channels are required to produce high quality daylight simulators in terms of the CIE Metamerism Indices. Furthermore, it is possible to create a spectrally tunable daylight simulator using six LED channels with wavelengths of 357, 420, 474, 533, 595, and 660 nm. This wavelength combination with appropriate intensity combinations can produce D50, D55, D65, and D75 simulators with good performance, in terms of the CIE Metamerism Indices. The color appearance of various color samples or objects under such a daylight simulator is found to be very similar to those under a corresponding daylight illuminant. © 2016 Wiley Periodicals, Inc. Col Res Appl, 42, 419–423, 2017     

Reliability of self-administered Work Ability Index questionnaire among Korean workers

Although the Work Ability Index (WAI) has been used in many countries, its reliability is yet to be validated in Korea. In our study, test–retest results of WAI total score, WAI category and seven subscales were compared. The correlation coefficients of WAI total score and subscales 1 and 2 between test and retest were 0.70, 0.80 and 0.63, respectively. The κ values on WAI category, subscales 4, 5, 6 and 7 were 0.52, 0.32, 0.31, 0.48 and 0.85, respectively. The results of our reliability test show that WAI scores of female, younger and private company workers were found to be higher than those of male, older and public company workers, respectively. We conclude that overall test–retest reliability of WAI in Korea is acceptable. Another notable observation from our study is that work ability dimension (subscales 1, 2 and 7) had a higher reliability, whereas health dimension (subscales 3–6) had a lower reliability.     

SBH: Super byte-aligned hybrid bitmap compression

Bitmap indexes are commonly used in data warehousing applications such as on-line analytic processing (OLAP). Storing the bitmaps in compressed form has been shown to be effective not only for low cardinality attributes, as conventional wisdom would suggest, but also for high cardinality attributes. Compressed bitmap indexes, such as Byte-aligned Bitmap Compression (BBC), Word-Aligned Hybrid (WAH) and several of their variants have been shown to be efficient in terms of both time and space, compared to traditional database indexes. In this paper, we propose a new technique for compressed bitmap indexing, called Super Byte-aligned Hybrid (SBH) bitmap compression, which improves upon the current state-of-the-art compression schemes. In our empirical evaluation, the query processing time of SBH was about five times faster than that of WAH, while the size of its compressed bitmap indexes was retained nearly close to that of BBC.     

Forecasting conditional climate-change using a hybrid approach

A novel approach is proposed to forecast the likelihood of climate-change across spatial landscape gradients. This hybrid approach involves reconstructing past precipitation and temperature using the self-organizing map technique; determining quantile trends in the climate-change variables by quantile regression modeling; and computing conditional forecasts of climate-change variables based on self-similarity in quantile trends using the fractionally differenced auto-regressive integrated moving average technique. The proposed modeling approach is applied to states (Arizona, California, Colorado, Nevada, New Mexico, and Utah) in the southwestern U.S., where conditional forecasts of climate-change variables are evaluated against recent (2012) observations, evaluated at a future time period (2030), and evaluated as future trends (2009–2059). These results have broad economic, political, and social implications because they quantify uncertainty in climate-change forecasts affecting various sectors of society. Another benefit of the proposed hybrid approach is that it can be extended to any spatiotemporal scale providing self-similarity exists.