Forecast Maps Research Articles

Prototype Spatio-temporal Predictive System of pest development of the codling moth, Cydia pomonella, in Kazakhstan

Summary A prototype for pest development stages forecasting is developed in Kazakhstan exploiting data from the geoinformation technologies and using codling moth as a model pest in apples. The basic methodology involved operational thermal map retrieving based on MODIS land surface temperature products and weather stations data, their recalculation into accumulated degree days maps and then into maps of the phases of the codling moth population dynamics. The validation of the predicted dates of the development stages according to the in-situ data gathered in the apple orchards showed a good predictivity of the forecast maps. Predictivity of the prototype can be improved by using daily satellite sensor datasets and their calibration with data received from a network of weather stations installed in the orchards.

Typology of Meteorological Weather Forecast Maps Printed in World Newspapers

ABSTRACTThis paper presents the results of the analysis of more than 150 different printed newspaper weather forecasting maps, representing 91 global, national, regional and local daily news periodicals from 33 countries in 4 continents. The classification attention focused both on the character of the localizing background layer of the analysed maps as well as on the meteorological thematic layer. The results identify 12 types of background localizing maps and 47 used types of presentation of meteorological forecast information, only 19 of which were used repeatedly in more than one newspaper. All the findings were documented on comparable cartographic models. Conclusions suitable for practical applications are demonstrated in the discussion.

Segmentation of Lung Ct Images using Cascaded Fully Convolutional Neural Networks

Interpretation of CT Lung images by the radiologist can be enhanced to a greater extent by automatic segmentation of nodules. The efficiency of this interpretation depends on the completeness and non-ambiguousness of the CT Lung images. Here, a fully automatic cascaded basis was proposed for CT Lung image segmentation. In this proposal a customized FCN was used feature extractions exploration from many visual scales and differentiate anatomy with a thick forecast map. Widespread experimental outcomes demonstrate that this technique can address the incompleteness in boundary and this technique can achieve best accuracy in segmentation of Lung CT Images when compared to other techniques which address the same area.

Validating an Operational Flood Forecast Model Using Citizen Science in Hampton Roads, VA, USA

Changes in the eustatic sea level have enhanced the impact of inundation events in the coastal zone, ranging in significance from tropical storm surges to pervasive nuisance flooding events. The increased frequency of these inundation events has stimulated the production of interactive web-map tracking tools to cope with changes in our changing coastal environment. Tidewatch Maps, developed by the Virginia Institute of Marine Science (VIMS), is an effective example of an emerging street-level inundation mapping tool. Leveraging the Semi-implicit Cross-scale Hydro-science Integrated System Model (SCHISM) as the engine, Tidewatch operationally disseminates 36-h inundation forecast maps with a 12-h update frequency. SCHISM’s storm tide forecasts provide surge guidance for the legacy VIMS Tidewatch Charts sensor-based tidal prediction platform, while simultaneously providing an interactive and operationally functional forecast mapping tool with hourly temporal resolution and a 5 m spatial resolution throughout the coastal plain of Virginia, USA. This manuscript delves into the hydrodynamic modeling and geospatial methods used at VIMS to automate the 36-h street-level flood forecasts currently available via Tidewatch Maps, and the paradigm-altering efforts involved in validating the spatial, vertical, and temporal accuracy of the model.

СУЧАСНІ МОЖЛИВОСТІ ГЕОІНФОРМАЦІЙНОГО КАРТОГРАФУВАННЯ ПРИРОДНИХ ФАКТОРІВ ВПЛИВУ НА ПОШИРЕННЯ ХВОРОБ НАСЕЛЕННЯ

The article is devoted to the consideration of existing possibilities of geoinformation mapping for the identification of natural factors of influence on the spread of diseases among the population. The author identified the features of maps of natural factors influencing the distribution of diseases of the population are determined as to the types thereof: estimate maps and forecast maps. The latter is due to natural-focal infections, which are relatively stable in time regarding the possibilities of infection with them. The necessity of perfection of the algorithm of cartographic modelling of natural factors of influence on the distribution of natural-focal infectious diseases among the population based on modern geoinformation technological solutions is substantiated. It is indicated on the features of three methodical schemes in the process of geoinformation creation of such maps regarding the use of specific cartographic materials. These are the using of small-scale maps; the using of large-scale maps on the basis of the original materials of the appropriate scale; the using of small- scale maps based on other small-scale maps and large-scale maps. On the basis of network technologies, in particular, modern technological solutions Web 2.0, the author determined the possibilities of geoinformation mapping of natural factors of influence on the distribution of diseases among the population. These are: improved database model (transition from relational model to object-relational variant); conducting of mathematical and cartographic modeling with all necessary techniques in the environment of a full-function geoinformation system; choosing the best options for presenting and distributing developed cartographic models among the more quantity of potential users; the presence of feedback with them. In the article, the author defined the specifications of Web 2.0, relevant for creating/using maps of the risk of infection of the population with natural focal diseases, with adapting to four groups technological solutions in relation to services (platform, software, data, infrastructure).

ОПЫТ МНОГОФАКТОРНОГО ЛОКАЛЬНОГО ПРОГНОЗИРОВАНИЯ НА ПРИМЕРЕ ГОЛЮБИНСКО-ОЛЛАМИНСКОГО НЕФРИТОНОСНОГО ПОЛЯ (РЕСПУБЛИКА БУРЯТИЯ)

The traditional formation and geotectonic method of forecasting is ineffective when carrying out prospect evaluation survey and exploration development works as these works are carried out on small-sized objects located within the same geotectonic region on the development area of one formation. Under these conditions, it is necessary to attract indirect prospecting factors that reflect the probabilistic nature of the relationship between the mineragenesis processes and geological complexes of various types. This is especially true for nephrite deposits, which generally belong to small and ultra-small objects with a very limited amount of reserves. This paper presents the experience of local multifactorial forecasting of the Golyubinsko-Ollaminskoe nephrite field. The low efficiency of traditional forecasting methods using mineragenic factors of the first kind is shown. It is proposed to carry out local forecasting of nephrite using the mineragenic factors of the second kind and indirect prospecting factors, which are not reflected on the source maps but are informative on the probability of the presence or absence of nephrite deposits. Their potential nephrite content is evaluated using the methods of mathematical statistics. Mineragenic zoning of the area is performed by compiling a multifactor forecast map with the specification of areas with varying degrees of prospects. The following phasing of local forecasting is implemented: the geological structure of the area is analyzed and nephrite-controlling structural and material complexes are classified as mineragenic factors of the second kind; indirect search features are identified; assessment is given to the informative content of various controlling factors and total informativeness of the sections of the combination of features of each category of factors. Single-factor forecast maps and a multi-factor forecast search map of the Golyubinsko-Ollominskoe field nephrite content is compiled. The proposed forecasting methods even on a limited amount of initial geological data allows area zoning according to the degree of prospects and identification of sites for nephrite search.

Reducing bias in multivariate analyses due to the modifiable areal unit problem.

IntroductionThe Modifiable Areal Unit Problem (MAUP) arises from the aggregation of data organized by spatially defined boundaries. Aggregated values are influenced by the shape (zone effect) and scale of the aggregated units. Aggregations of the same data using different zones or scales can give different analytical results, none reliable.
 Objectives and ApproachUsing population-level administrative health data in Western Australia, the objectives were to: accurately measure the association between health service utilization and demographic, socio-economic, and service accessibility variables; and develop models to accurately forecast areas of high health service utilization into the future.
 Multiple zone designs and aggregation scales were used to examine the impact of MAUP in association studies. These zone designs and scales were then used in all-subset model selection processes, combined with repeated k-fold cross-validation, to generate forecast maps of areas having high future rates of health service utilization.
 ResultsThe impact of the MAUP and methods to reduce this bias in association studies will be presented, for both simple and complex model designs. Maps indicating gradients of predicted probabilities of high rate of health service demand in the future can be used to optimize the placement of services, through the use of catchment areas based on road-network travel distance and population distributions.
 Conclusion/ImplicationsThe impact of the MAUP on the analysis of spatially-aggregated data has been considered intractable. However, methods to reduce the impact of the MAUP can improve policy and planning decisions based on such studies.

A Hydraulic MultiModel Ensemble Framework for Visualizing Flood Inundation Uncertainty

AbstractWhile deterministic forecasts provide a single realization of potential inundation, the inherent uncertainty associated with forecasts also needs to be conveyed for improved decision support. The objective of this study was to develop an ensemble framework for the quantification and visualization of uncertainty associated with flood inundation forecast maps. An 11‐member ensemble streamflow forecast at lead times from 0 to 48 hr was used to force two hydraulic models to produce a multimodel ensemble. The hydraulic models used are (1) the International River Interface Cooperative along with Flow and Sediment Transport with Morphological Evolution of Channels solver and (2) the two‐dimensional Hydrologic Engineering Center‐River Analysis System. Uncertainty was quantified and augmented onto flood inundation maps by calculating statistical spread among the ensemble members. For visualization, a series of probability flood maps conveying the uncertainty in forecasted water extent, water depth, and flow velocity was disseminated through a web‐based decision support tool. The results from this study offer a framework for quantifying and visualizing model uncertainty in forecasted flood inundation maps.

Виділення перспективних нафтогазоносних об'єктів південно-східної частини Дніпровсько-Донецького авлакогену та основи застосування результатів геофізичних досліджень

To ensure complete refusal of gas import by Ukraine, it is necessary to pay more attention to the search of unconventional sources of hydrocarbons and involve the territories previously considered insufficiently promising or unpromising. One of such promising gas-bearing fields is the south-eastern part of the Dniprovo-Donetskyi avlakogene (DDA). For the allocation of promising oil and gas bearing areas in south-east part of the DDA the author proposed to use the vitrinite reflectivity. For some fields of DDA, the author calculated and developed graphs of vitrinite reflectivity gradient with depth value, the forecast map of the lateral and vertical distribution of the vitrinite reflectivity coefficient at 5 km depth was developed. According to the research results of geological structure in the south-east part of the DDA, preliminary estimates of kerogen catagenetic maturity were performed concerning generation and accumulation of hydrocarbons, in particular, in avlakogene consolidated fissured rocks. Determination methods of the organic substance content and prediction of gas-bearing perspective fine-grained clay-silty rocks with a high content of kerogen were considered by the complex of geophysical research methods.

Emphasizing Grid Feature as Flood Zonal Identification to Support Flood Relief Mechanism

Grid in Geographic Information System [GIS] mapping is used to determine specific location. This is a study to highlight the utilization of square grids network with flood forecast map modelling for regional identification of inundated zones focusing on Pekan sub-district. Satellite information, raster and digital elevation model [DEM] are used to produce a flood prediction map model to simulate inundation of the study area. Vertical elevation has been converted to match between WMGEOID04 with EGM96 ellipsoidal height for the multiple offsets of water layers in inundation simulation process because geoid and mean seal level values are set to be the same. Inundated zones are populated based on selected readings of Sungai Pahang gauging station located at Department of Irrigation and Drainage [DID] Pulau Jawa station, Bandar Pekan [Station ID: 3434401]. Flood spreads are monitored based on the 1.0m layer offsets starting from 2.5m [alert level] until 5.5m. Finally, square grids tessellation of the whole area within the boundary is generated and the affected zones are projected in different color code to indicate danger zones. The graphical output that are produced are able to be used to assess the rate of accessibility, habitability, and affected infrastructures which have the potential to act as flood relief center. Integrating these information with disaster web provider like ‘InfoBanjir’ [DID] and ‘disaster portal’ [NADMA] should be emphasized to ensure systematic flood management in the future.

PEMANFAATAN CITRA MODIS LEVEL 1B DALAM PEMBUATAN PETA PRAKIRAAN DAERAH PENANGKAPAN IKAN (Studi Kasus : Pantai Selatan Blitar)

The potential of marine resources in Blitar quite large but utilization is not optimal, so it requires a Regional Forecast Map Fishing in order to exploit the potential of fishery resources there is more optimal. Determination of the area forecast use of the fish can be predicted from the parameters of sea surface temperature and chlorophyll-a. Modis image level 1b can be used in the manufacture of the forecast utilization of fish pennagkapan area. Used algorithm ATBD (Theoretical Basic Document Encryption Modis) 25 to determine sea surface temperature and Algorithms Morel 4 is used to determine the chlorophyll-a. The results obtained from this study is the RMSE test conducted between field data with the data Modis image of RMSE 1b level of 0.706568, with forecasts of fishing areas and potentially potent enough.

Ship voyage optimization for safe and energy-efficient navigation: A dynamic programming approach

The paper presents a 3D dynamic programming based ship voyage optimization method, aiming to select the optimal path and speed profile for a ship voyage on the basis of weather forecast maps. The optimization is performed in accordance to a minimum fuel consumption strategy taking also into account ship motions and comfort. The optimization is carried out in a discretized space-time domain: the ship voyage is parametrized as a multi-stage decision process in order to formulate a dynamic programming optimization problem. Waves and wind conditions are estimated for each route segment by weather forecasting maps then seakeeping related indexes and fuel oil consumption are computed taking into account wave-induced ship motions and added resistance. The best routing solution is thus selected by a dynamic programming algorithm developed and implemented by the authors. Results and discussion of the proposed method are presented for a merchant ship application in a test case voyage through the Northern Atlantic Ocean and compared to the constant speed great circle solution.

Evaluation of seismic potential in a longwall mine with massive sandstone roof under deep overburden

A recent seismic event was recorded by a deep longwall mine in Virginia at 3.7 ML on the local magnitude scale and 3.4 MMS by the United States Geological Survey (USGS) in 2016. Further investigations by the National Institute for Occupational Safety and Health (NIOSH) and Coronado Coal researchers have shown that this event was associated with geological features that have also been associated with other, similar seismic events in Virginia. Detailed mapping and geological exploration in the mining area has made it possible to forecast possible locations for future seismic activity. In order to use the geology as a forecaster of mining-induced seismic events and their energy potential, two primary components are needed. The first component is a long history of recorded seismic events with accurately plotted locations. The second component is a high density of geologic data within the mining area. In this case, 181 events of 1.0 ML or greater were recorded by the mine’s seismic network between January, 2009, and October, 2016. Within the mining area, 897 geophysical logs, 224 core holes, and 1031 fiberscope holes were examined by mine geologists. From this information, it was found that overburden thickness, sandstone thickness, and sandstone quality contributed greatly to seismic locations. After the data was analyzed, a pattern became apparent indicating that the majority of seismic events occurred under specific conditions. Three forecast maps were created based on geology of previous seismic locations. The forecast maps have shown an accuracy of within 74%–89% when compared to the recorded 181 events that were 1.0 ML or greater when considering three major geological criteria of overburden thickness of 579.12 m or greater, 6.096–12.192 m of sandstone within 15.24 m of the Pocahontas number 3 seam, and a longwall caving height of 4.572 m or less.

Assessment of water inrush and factor sensitivity analysis in an amalgamated coal mine in China

Hydrogeological data are generally incomplete and inaccurate in amalgamated coal mines in China, which results in inaccuracy in water inrush forecasts. To enhance the precision of the prediction of water inrush from coal floor in an amalgamated coal mine, the vulnerability index method was developed using an analytic hierarchy process (AHP) to analyze the water inrush hazard. Six factors related to water inrush were selected and the corresponding single factor thematic map was established through geographic information system (GIS). The AHP model was built to calculate the weight of each factor. The final forecast map based on vulnerability index was acquired by superposing the six thematic maps. The forecast map was consistent with the real water inrush position. The sensitivity of the six factors was analyzed and the water-resisting layer played a significant role in controlling water inrush. Several suggestions about water inrush prevention were put forward based on the prediction results.

USA National Phenology Network's volunteer-contributed observations yield predictive models of phenological transitions.

PurposeIn support of science and society, the USA National Phenology Network (USA-NPN) maintains a rapidly growing, continental-scale, species-rich dataset of plant and animal phenology observations that with over 10 million records is the largest such database in the United States. The aim of this study was to explore the potential that exists in the broad and rich volunteer-collected dataset maintained by the USA-NPN for constructing models predicting the timing of phenological transition across species’ ranges within the continental United States. Contributed voluntarily by professional and citizen scientists, these opportunistically collected observations are characterized by spatial clustering, inconsistent spatial and temporal sampling, and short temporal depth (2009-present). Whether data exhibiting such limitations can be used to develop predictive models appropriate for use across large geographic regions has not yet been explored.MethodsWe constructed predictive models for phenophases that are the most abundant in the database and also relevant to management applications for all species with available data, regardless of plant growth habit, location, geographic extent, or temporal depth of the observations. We implemented a very basic model formulation—thermal time models with a fixed start date.ResultsSufficient data were available to construct 107 individual species × phenophase models. Remarkably, given the limited temporal depth of this dataset and the simple modeling approach used, fifteen of these models (14%) met our criteria for model fit and error. The majority of these models represented the “breaking leaf buds” and “leaves” phenophases and represented shrub or tree growth forms. Accumulated growing degree day (GDD) thresholds that emerged ranged from 454 GDDs (Amelanchier canadensis-breaking leaf buds) to 1,300 GDDs (Prunus serotina-open flowers). Such candidate thermal time thresholds can be used to produce real-time and short-term forecast maps of the timing of these phenophase transition. In addition, many of the candidate models that emerged were suitable for use across the majority of the species’ geographic ranges. Real-time and forecast maps of phenophase transitions could support a wide range of natural resource management applications, including invasive plant management, issuing asthma and allergy alerts, and anticipating frost damage for crops in vulnerable states.ImplicationsOur finding that several viable thermal time threshold models that work across the majority of the species ranges could be constructed from the USA-NPN database provides clear evidence that great potential exists this dataset to develop more enhanced predictive models for additional species and phenophases. Further, the candidate models that emerged have immediate utility for supporting a wide range of management applications.

SBAS-derived TEC maps: a new tool to forecast the spatial maps of maximum probable scintillation index over India

With the increase in the use of satellite-based navigation services, the forecasting of L band scintillation has turned out to be of paramount importance as it affects their accuracy and availability. Forecasting the time of occurrence or non-occurrence, strength and probable location of scintillation enables the service providers and users to take appropriate action to mitigate the effects and optimize the services. We use the recently developed method to retrieve TEC from the ionospheric correction data transmitted by the Indian satellite-based augmentation system (SBAS)–GAGAN. By making use of the established linear relation between the dusk time TEC and the maximum probable scintillation intensity (S4max), scintillation forecast maps have been generated as early as 1930 LT. The superposition of actual S4 measurements, obtained from the GAGAN network of receivers, on the forecasted S4max map shows that the actual measurements are less than the predicted S4max except on very few occasions. The potential of the simple technique to predict the 2 D maps of maximum probable scintillation index for the whole night has been demonstrated which with more refinements could evolve into a viable forecast or forewarning system.

Geobotanical forecasting in the nature management ecological optimization in Baikalian Siberia

An analysis is made of the geobotanical forecasting technique. Mapping is known to be recognized as the most powerful method of forecasting-geobotanical investigations. A close interrelation between geobotanical forecasting and vegetation mapping is demonstrated. To improve objectivity of the findings used cartographic models of modern and predicted vegetation which most fully represent the entire structuralcenotic diversity of plant communities in Prebaikalia, the central region of Baikalian Siberia. The predictive constructions are based on analyzing modern vegetation which always includes structural features of its future that manifest themselves in primary and stable derivative communities. We developed the legend and compiled the map (sc 1:200 000) of the region’s modern vegetation. The state of the natural environment of the Lena- Angara interfluve is assessed having regard to the various functions of plant communities in geosystems. The 1:200 000 map of probability-forecasted (within 200 years) vegetation is presented for Lena-Angara interfluve. It is shown that complete recovery of the primary structure will not occur within 200 years. It is found that it is necessary to carry out predictive-cartographic investigations for the whole of Baikalian Siberia as vegetation of this region is characterized by a decreased activity of recovery processes occurring under the influence of anthropogenic factors. The reason has to do with the harsh natural conditions where it is not worthwhile to pursue extensive nature management, such as is universally the case to date, leading to undesirable economic consequences, because the degree of disturbance to vegetation in a number of areas of Prebaikalia has now reached as critical level. It is concluded that a further policy of nature management on this territory must be built upon the new principles to be approved by government documents.

Constitutive Modeling for Thixoforming of 9Cr18 Semi-solid Alloy and 3D Forecast Mapping

Thixotropic compression tests were carried out on 9Cr18 semi-solid alloy through Gleeble-1500 thermal simulation machine. According to the experiment analysis, macro separation occurred during thixoforming. The liquid film was extruded outside to the surface and solidified to form eutectic structure. The solid particles were connected with each other and underwent plastic deformation. According to the comparison between Zhou-Guan model and modified Zhou-Guan model, it could be observed that the adding of thixotropic factor played an important role in the regression and the latter one was more credible. The modified Zhou-Guan model could well describe the thixoforming behavior. 3D forecast mapping was built for 9Cr18 semi-solid alloy in thixoforming temperature range. It would provide valuable information for selecting process parameters during thixoforming in the manufacture process.

Initial skill assessment of the California Harmful Algae Risk Mapping (C-HARM) system

Toxic algal events are an annual burden on aquaculture and coastal ecosystems of California. The threat of domoic acid (DA) toxicity to human and wildlife health is the dominant harmful algal bloom (HAB) concern for the region, leading to a strong focus on prediction and mitigation of these blooms and their toxic effects. This paper describes the initial development of the California Harmful Algae Risk Mapping (C-HARM) system that predicts the spatial likelihood of blooms and dangerous levels of DA using a unique blend of numerical models, ecological forecast models of the target group, Pseudo-nitzschia, and satellite ocean color imagery. Data interpolating empirical orthogonal functions (DINEOF) are applied to ocean color imagery to fill in missing data and then used in a multivariate mode with other modeled variables to forecast biogeochemical parameters. Daily predictions (nowcast and forecast maps) are run routinely at the Central and Northern California Ocean Observing System (CeNCOOS) and posted on its public website. Skill assessment of model output for the nowcast data is restricted to nearshore pixels that overlap with routine pier monitoring of HABs in California from 2014 to 2015. Model lead times are best correlated with DA measured with solid phase adsorption toxin tracking (SPATT) and marine mammal strandings from DA toxicosis, suggesting long-term benefits of the HAB predictions to decision-making. Over the next three years, the C-HARM application system will be incorporated into the NOAA operational HAB forecasting system and HAB Bulletin.

Visualizing Long-Range Severe Thunderstorm Environment Guidance from CFSv2

Abstract Two novel approaches to extending the range of prediction for environments conducive to severe thunderstorm events are described. One approach charts Climate Forecast System, version 2 (CFSv2), run-to-run consistency of the areal extent of severe thunderstorm environments using grid counts of the supercell composite parameter (SCP). Visualization of these environments is charted for each 45-day CFSv2 run initialized at 0000 UTC. CFSv2 ensemble-mean forecast maps of SCP coverage over the contiguous United States are also produced for those forecasts meeting certain criteria for high-impact weather. The applicability of this approach to the severe weather prediction challenge is illustrated using CFSv2 output for a series of severe weather episodes occurring in March and April 2014. Another approach, possibly extending severe weather predictability from CFSv2, utilizes a run-cumulative time-averaging technique of SCP grid counts. This process is described and subjectively verified with severe weather events from early 2014.