Artificial intelligence and spatial Modelling in natural hazards and environmental applications
Advances in Science, Technology and Innovation, ISSN: 2522-8722, Page: 7-10
2019
- 1Citations
- 23Captures
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Book Chapter Description
Modeling and predicting geohazards is extremely difficult due to their complex behavior in the real-world. In fact, several aspects of these environmental applications are considered in computer-based modeling to accurately estimating real-world phenomena. Till date, none of the proposed methods have reached to zero uncertainties or errors to recognize the entire disaster’s events. Globally, many people have lost their lives due to various types of natural hazards. Therefore, it is important to detect, monitor and predict them to protect the inhabitants against the potential natural hazards that threaten human lives and properties. Recently, artificial intelligent (AI) methods have received a great deal of attraction due to their precision to model the complex problems such as natural hazards. AI can see different aspects of a complex problem with sufficient iteration and details. In recent years, implementation of AI models coupled with geospatial information systems (GIS) are the most efficient and accurate approach to model natural disasters i.e. flooding, earthquake, landslides, forest fire and drought rather than other existing methods. This gives an insight into the ability of applied AI models in some natural hazards applications.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85108060639&origin=inward; http://dx.doi.org/10.1007/978-3-030-01440-7_3; http://link.springer.com/10.1007/978-3-030-01440-7_3; http://link.springer.com/content/pdf/10.1007/978-3-030-01440-7_3; https://dx.doi.org/10.1007/978-3-030-01440-7_3; https://link.springer.com/chapter/10.1007/978-3-030-01440-7_3
Springer Science and Business Media LLC
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know