Comparison of artificial intelligence algorithms to estimate sustainability indicators
| Autor: | David Bienvenido-Huertas, Rui Lança, Elisa M. J. Silva, Miguel Oliveira, Fátima Farinha |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Algarve (Portugal)
Política medioambiental Artificial intelligence Energy & Fuels Computer science Geography Planning and Development 0211 other engineering and technologies 1206.01 Construcción de Algoritmos Transportation 02 engineering and technology Redes neuronales 010501 environmental sciences Minería de datos 01 natural sciences OBSERVE platform Linear regression 2501.21 Simulación Numérica Sustainability indicators 021108 energy Data mining 0105 earth and related environmental sciences Civil and Structural Engineering Desarrollo sostenible Sustainable development Science & Technology Algoritmos Renewable Energy Sustainability and the Environment business.industry 1207.10 Redes de Flujo Sostenibilidad Random forest Monitoring process 3308.01 Control de la Contaminación Atmosférica 5902.08 Política del Medio Ambiente Multilayer perceptron 1203.04 Inteligencia Artificial Sustainability Monitorización Construction & Building Technology Medio ambiente business Inteligencia Artificial Algorithm |
| Zdroj: | RIARTE Consejo General de la Arquitectura Técnica de España (CGATE) Repositório Científico de Acesso Aberto de Portugal Repositório Científico de Acesso Aberto de Portugal (RCAAP) instacron:RCAAP |
| Popis: | the monitoring of sustainability indicators allows behavioural tendencies of a region to be controlled, so that adequate policies could be established in advance for a sustainable development. However, some data could be missed in the monitoring of these indicators, thus making the establishment of sustainability policies difficult. This paper therefore analyses the possibility to forecast the sustainability indicators of a region by using four different artificial intelligent algorithms: linear regression, multilayer perceptron, random forest, and M5P. the study area selected was the Algarve region in Portugal, and 180 monitored indicators were analysed between 2011 and 2017. the results showed that M5P is the most appropriate algorithm to estimate sustainability indicators. M5P was the algorithm obtaining the best estimations in a greater number of indicators. Nevertheless, the results showed that MP5 was not the best option for all indicators, since in some of them, the use of other algorithms obtained better results, thus reflecting the need of an individual previous study of each indicator. With these algorithms, it is possible for public bodies and institutions to evaluate the sustainable development of the region and to have reliable information to take corrective measures when needed, thus contributing to a more sustainable future. Operational Program CRESC ALGARVE 2020 [ALG-01-0246-FEDER-027503] info:eu-repo/semantics/publishedVersion |
| Databáze: | OpenAIRE |
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