Combining Artificial Neural Networks and GIS Fundamentals for Coastal Erosion Prediction Modeling
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41330%2F19%3A79605" target="_blank" >RIV/60460709:41330/19:79605 - isvavai.cz</a>
Result on the web
<a href="https://www.mdpi.com/2071-1050/11/4/975/htm" target="_blank" >https://www.mdpi.com/2071-1050/11/4/975/htm</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3390/su11040975" target="_blank" >10.3390/su11040975</a>
Alternative languages
Result language
angličtina
Original language name
Combining Artificial Neural Networks and GIS Fundamentals for Coastal Erosion Prediction Modeling
Original language description
The complexities of coupled environmental and human systems across the space and time of fragile systems challenge new data driven methodologies. Combining geographic information systems and artificial neural networks allows us to design a model that forecasts the erosion changes in Costa da Caparica, Lisbon, Portugal, for 2021, with a high accuracy level. The developed model proves to be a powerful tool, as it analyzes and provides the where and the why dynamics that have happened or will happen in the future. According to the literature, Artificial Neural Networks present noteworthy advantages compared to the other methods that are used for prediction and decision making in urban coastal areas. In order to conduct a sensitivity analysis on natural and social forces, as well as dynamic relations in the dune beach system of the study area, two types of ANNs were tested on a GIS environment: radial basis function and multilayer perceptron. This model helps to understand the factors that impac
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10511 - Environmental sciences (social aspects to be 5.7)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Applied Geography
ISSN
0143-6228
e-ISSN
2071-1050
Volume of the periodical
11
Issue of the periodical within the volume
4
Country of publishing house
GB - UNITED KINGDOM
Number of pages
14
Pages from-to
1-14
UT code for WoS article
000460819100035
EID of the result in the Scopus database
2-s2.0-85061580857