BIM Big Data Storage in WebVRGIS Lv, Zhihan; Li, Xiaoming; Lv, Haibin ...
IEEE transactions on industrial informatics,
04/2020, Letnik:
16, Številka:
4
Journal Article
In the context of big data and the Internet of Things, with the advancement of geospatial data acquisition and retrieval, the volume of available geospatial data is increasing every minute. Thus, new ...data-management architecture is needed. We proposed a building information model (BIM) big data-storage-management solution with hybrid storage architecture based on web virtual reality geographical information system (WebVRGIS). BIM is associated with the integration of spatial and semantic information on the various stages of urban building. In this paper, based on the spatial distribution characteristics of BIM geospatial big data, a data storage and management model is proposed for BIM geospatial big data management. The architecture primarily includes Not only Structured Query Language (NoSQL) database and distributed peer-to-peer storage. The evaluation of the proposed storage method is conducted on the same software platform as our previous research about WebVR. The experimental results show that the hybrid storage architecture proposed in this research has a lower response time compared to the traditional relational database in geospatial big data searches. The integration and fusion of BIM big data in WebVRGIS realizes a revolutionary transformation of city information management during a full lifecycle. The system also has great promise for the storage of other geospatial big data, such as traffic data.
The main objective of the present study was to compare the performance of a classifier that implements the Logistic Regression and a classifier that employs a Naïve Bayes algorithm in landslide ...susceptibility assessments. The study provides an evaluation concerning the influence of model's complexity and the size of the training data, while it identifies the most accurate and reliable classifier.
The comparison of the two classifiers was based on the assessment of a database containing 116 sites located at the mountains of Epirus, Greece, where serious landslides events have been encountered. The sites are classified into two categories, non-landslide and landslide areas. The identification of those areas was established by analysing airborne imagery, extensive field investigation and the examination of previous research studies. The geo-environmental conditions in those locations where analyzed in regard with their susceptibility to slide. In particular, seven variables where analyzed: engineering geological units, slope angle, slope aspect, mean annual rainfall, distance from river network, distance from tectonic features and distance from road network.
Multicollinearity analysis and feature selection was implemented in order to estimate the conditional independence among the variables and to rank the variables according to their significance in estimating landslide susceptibility. By the above processes the construction of nine different datasets was accomplished. Further partition allowed creating subsets of training and validating data from the original 116 sites. Each dataset was characterized by the number of the variables used and the size of the training datasets.
The comparison and validation of the outcomes of each model was achieved using statistical evaluation measures, the receiving operating characteristic and the area under the success and predictive rate curves. The results indicated that model's complexity and the size of the training dataset influence the accuracy and the predictive power of the models concerning landslide susceptibility. In particular, the most accurate model with high predictive power was the eighth model (five variables and 92 training data), with the Naïve Bayes classifier having a slightly higher overall performance and accuracy than the Logistic Regression classifier, 87.50% and 82.61% on the validation datasets, respectively. The highest area under the curve was achieved by the Naïve Bayes classifier for both the training and validating datasets (0.875 and 0.806 respectively) while the Logistic Regression classifier achieved a lower AUC values for the training and validating datasets (0.844 and 0.711, respectively). When limited data are available it seems that more accurate and reliable results could be obtained by generative classifiers, like Naïve Bayes classifiers. Overall, landslide susceptibility assessments could serve as a useful tool for the local and national authorities, in order to evaluate strategies to prevent and mitigate the adverse impacts of landslide events.
•Logistic regression and Naïve Bayes were used in landslide susceptibility zoning.•Model complexity and the size of training data influence the prediction accuracy.•The reduction in model's complexity improved the generalization performance.•The Naïve Bayes model outperforms the Logistic regression.
The present study was conducted on the river Yamuna, which passes through Delhi-NCR from Baghpat to Chhainssa, a distance of about 125 km, at six sampling locations to evaluate the concentrations of ...heavy metals in surface water using heavy metal pollution index (HPI) approach. The river serves both urban-industrial and rural areas in the study area; hence, domestic, industrial, and agricultural wastes are being contributed greatly in the contamination of river water. The Yamuna River is one of the major tributaries of the river Ganga originated in the Himalayas and is flowing through a varied geological terrain. Metals such as iron (Fe), copper (Cu), cobalt (Co), zinc (Zn), lead (Pb), cyanide (CN), nickel (Ni), and chromium (Cr) in selected sites of Yamuna River water were determined by using atomic absorption spectrophotometer. The concentrations of Fe, Cu, Co, Zn, Pb, CN, Ni, and Cr in the river water were found to be in the range of 40–190, 50–120, 4–66, 840–1800, 2–40, 100–600, 88–253, and 35–52 μg/L, respectively. The results show that the maximum heavy metal content was found at sampling site S3 (Nizamuddin) followed by S6 (Chhainssa), S4 (Okhla), S1 (Baghpat), S5 (Manjhawali), and S2 (Pachahira). The heavy metal data was integrated in GIS environment for preparing spatial distribution maps of sampling sites. A scatter plot matrix was created to assess the pattern and interrelationships between heavy metals. The average concentration of heavy metals was recorded high, often exceeding the permissible limits for drinking of surface water prescribed by the Bureau of Indian Standards (BIS) and World Health Organization (WHO). Based on HPI (varies from 98.2 to 555.1), about 85% of the river water was classified as highly polluted; hence, it is not recommended for drinking. Overall, significant variations were observed in concentrations of heavy metals from one location to the other which may be because of toxic industrial effluents and domestic sewage wastes being added to the river water by various anthropogenic activities in the study area. The present work highlights the pollution load of heavy metals in the river Yamuna and also advocates an urgent attention towards minimizing the health risk of people residing not only along the river banks and surrounding regions but also for city population.
This open access book provides an overview of the progress in landslide research and technology and is part of a book series of the International Consortium on Landslides (ICL). The book provides a ...common platform for the publication of recent progress in landslide research and technology for practical applications and the benefit for the society contributing to the Kyoto Landslide Commitment 2020, which is expected to continue up to 2030 and even beyond to globally promote the understanding and reduction of landslide disaster risk, as well as to address the 2030 Agenda Sustainable Development Goals.
The Himalayan region and hilly areas face severe challenges due to landslide occurrences during the rainy seasons in India, and the study area, i.e., the Rudraprayag district, is no exception. ...However, the landslide related database and research are still inadequate in these landslide-prone areas. The main purpose of this study is: (1) to prepare the multi-temporal landslide inventory map using geospatial platforms in the data-scarce environment; (2) to evaluate the landslide susceptibility map using weights of evidence (WoE) method in the Geographical Information System (GIS) environment at the district level; and (3) to provide a comprehensive understanding of recent developments, gaps, and future directions related to landslide inventory, susceptibility mapping, and risk assessment in the Indian context. Firstly, 293 landslides polygon were manually digitized using the BHUVAN (Indian earth observation visualization) and Google Earth® from 2011 to 2013. Secondly, a total of 14 landslide causative factors viz. geology, geomorphology, soil type, soil depth, slope angle, slope aspect, relative relief, distance to faults, distance to thrusts, distance to lineaments, distance to streams, distance to roads, land use/cover, and altitude zones were selected based on the previous study. Then, the WoE method was applied to assign the weights for each class of causative factors to obtain a landslide susceptibility map. Afterward, the final landslide susceptibility map was divided into five susceptibility classes (very high, high, medium, low, and very low classes). Later, the validation of the landslide susceptibility map was checked against randomly selected landslides using IDRISI SELVA 17.0 software. Our study results show that medium to very high landslide susceptibilities had occurred in the non-forest areas, mainly scrubland, pastureland, and barren land. The results show that medium to very high landslide susceptibilities areas are in the upper catchment areas of the Mandakini river and adjacent to the National Highways (107 and 07). The results also show that landslide susceptibility is high in high relative relief areas and shallow soil, near thrusts and faults, and on southeast, south, and west-facing steep slopes. The WoE method achieved a prediction accuracy of 85.7%, indicating good accuracy of the model. Thus, this landslide susceptibility map could help the local governments in landslide hazard mitigation, land use planning, and landscape protection.
Decentralized solar PhotoVoltaic (PV) is one of the most promising energy sources for cities and individuals pursuing energy self-sufficiency. Especially, the already available rooftop surfaces are a ...major contributor to push for rooftop mounted PV systems. However, accurate PV potential estimation of individual buildings is still a challenging task since many parameters must be considered such as meteorological factors, panel technology, geographical location, available roof surface area, surface azimuth and tilt angle. In this study, we created an efficient approach that can be used for roof surface's PV potential estimation based on point cloud data and capable of processing various scales from single building to city scale. In the proposed approach, each roof surface's features were utilized for PV potential estimation by employing the PVGIS database. PV potential estimation was carried out on daily, monthly, and annual periods to provide a better estimation. Also, we developed a flexible and easy to use open-source plugin based on the QGIS software for rooftop mounted PV potential estimation capable of estimating every roof surface's PV potential. The method was tested on 80 buildings selected from ROOFN3D dataset. The proposed approach achieved an overall accuracy of 84% and an F1 score of 0.92.
Mastering QGIS Van Hoesen, John; Menke, Kurt; Smith, Richard ...
2015., 2015
eBook
QGIS is the leading alternative to proprietary GIS software. Although QGIS is described as intuitive, it is also, by default, complex. Knowing which tools to use and how to apply them is essential to ...producing valuable deliverables on time. Starting with a refresher on QGIS basics, this book will take you all the way through to creating your first custom QGIS plugin. By the end of the book, you will understand how to work with all the aspects of QGIS, and will be ready to use it for any type of GIS work. From the refresher, you will learn how to create, populate, and manage a spatial database and walk through styling GIS data, from creating custom symbols and color ramps to using blending modes. In the next section, you will discover how to prepare vector and raster data for processing and discover advanced data creation and editing techniques. The last third of the book covers more technical aspects of QGIS, including working with the Processing Toolbox, how to automate workflows with batch processing, and how to create graphical models. Finally, you will learn how to create and run Python data processing scripts and write your own QGIS plugin with pyqgis.
With the help of satellite data and numerical geographical information system (GIS) methods, the total capacity of dew volume on the entire territory of the Republic of Serbia was estimated. ...Multicriteria GIS analysis and satellite detections with the support of methods such as kriging and semi‐kriging gave satisfactory results in the present research. After the download of satellite data, they were compared with meteorological data for precipitation, evaporation and air temperature. A very precise grid in 1 × 1° of longitude and latitude was created. The average estimated dew potential for the territory of Serbia is 20–40 mm⋅year−1 for the south of the country, 15 mm⋅year−1 for the north, 30–50 mm⋅year−1 for the central region and 20–30 mm⋅year−1 for the east. In most drought regions, it is < 10 mm⋅year−1⋅m−2. Counties with the largest dew capacity (between 15,200 and 20,000 L) include Borski, Nišavski and Jablanički in the eastern part of the country, as well as Zlatiborski, Raški and Peć in the western and southern parts, respectively. On the other hand, counties with the lowest dew capacity (2,000–3,000 L) encompass northern parts of Serbia (Sremski, Severno‐Banatski, Srednje‐Banatski, Južno‐Banatski, Severno‐Bački and Zapadno‐Bački). The possibility for dew use is particularly strong during the spring. The estimated total capacity of the dew potential for Serbia is 1.5 × 107 L. By comparing the obtained data for Serbia, it is concluded that the amount of this type of water resource is not large, but enough for use in agricultural and other economic sectors.
Three sub‐numerical methods for dew volume calculations.
During the years 2001–2005, a European solar radiation database was developed using a solar radiation model and climatic data integrated within the Photovoltaic Geographic Information System (PVGIS). ...The database, with a resolution of 1
km
×
1
km, consists of monthly and yearly averages of global irradiation and related climatic parameters, representing the period 1981–1990. The database has been used to analyse regional and national differences of solar energy resource and to assess the photovoltaic (PV) potential in the 25 European Union member states and 5 candidate countries. The calculation of electricity generation potential by contemporary PV technology is a basic step in analysing scenarios for the future energy supply and for a rational implementation of legal and financial frameworks to support the developing industrial production of PV. Three aspects are explored within this paper: (1) the expected average annual electricity generation of a ‘standard’ 1
kW
p grid-connected PV system; (2) the theoretical potential of PV electricity generation; (3) determination of required installed capacity for each country to supply 1% of the national electricity consumption from PV. The analysis shows that PV can already provide a significant contribution to a mixed renewable energy portfolio in the present and future European Union.
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