Combining Artificial Neural Networks and GIS Fundamentals for Coastal Erosion Prediction Modeling

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Combining Artificial Neural Networks and GIS Fundamentals for Coastal Erosion Prediction Modeling

Popis výsledku v původním jazyce

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

Název v anglickém jazyce

Combining Artificial Neural Networks and GIS Fundamentals for Coastal Erosion Prediction Modeling

Popis výsledku anglicky

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

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10511 - Environmental sciences (social aspects to be 5.7)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Applied Geography

ISSN

0143-6228

e-ISSN

2071-1050

Svazek periodika

11

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

14

Strana od-do

1-14

Kód UT WoS článku

000460819100035

EID výsledku v databázi Scopus

2-s2.0-85061580857