Publications tagged with "Campania"

1. Ensemble Models for Predicting CO Concentrations: Application and Explainability in Environmental Monitoring in Campania, Italy

   Campanile, L., Di Bonito, L. P., Natale, F. D., & Iacono, M. (2024). Ensemble Models for Predicting CO Concentrations: Application and Explainability in Environmental Monitoring in Campania, Italy [Conference paper]. Proceedings - European Council for Modelling and Simulation, ECMS, 38(1), 558–564. https://doi.org/10.7148/2024-0558

   Abstract

   Monitoring of non-linear phenomena, such as pollution dynamics, which is the result of several combined factors and the evolution of environmental conditions, greatly benefits by AI tools; a larger benefit derives by the application of explainable solutions, which are capable of providing elements to understand those dynamics for better informed decisions. In this paper we discuss a case with real data in which a posteriori explanations have been produced after the application of ensemble models. © ECMS Daniel Grzonka, Natalia Rylko, Grazyna Suchacka, Vladimir Mityushev (Editors) 2024.

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   {"key"=>"Campanile2024558", "type"=>"Conference paper", "bibtex"=>"@conference{Campanile2024558,\n  author = {Campanile, Lelio and Di Bonito, Luigi Piero and Natale, Francesco Di and Iacono, Mauro},\n  title = {Ensemble Models for Predicting CO Concentrations: Application and Explainability in Environmental Monitoring in Campania, Italy},\n  year = {2024},\n  journal = {Proceedings - European Council for Modelling and Simulation, ECMS},\n  volume = {38},\n  number = {1},\n  pages = {558 – 564},\n  doi = {10.7148/2024-0558}\n}\n", "author"=>"Campanile, Lelio and Di Bonito, Luigi Piero and Natale, Francesco Di and Iacono, Mauro", "author_array"=>[{"first"=>"Lelio", "last"=>"Campanile", "prefix"=>"", "suffix"=>""}, {"first"=>"Luigi Piero", "last"=>"Di Bonito", "prefix"=>"", "suffix"=>""}, {"first"=>"Francesco Di", "last"=>"Natale", "prefix"=>"", "suffix"=>""}, {"first"=>"Mauro", "last"=>"Iacono", "prefix"=>"", "suffix"=>""}], "author_0_first"=>"Lelio", "author_0_last"=>"Campanile", "author_0_prefix"=>"", "author_0_suffix"=>"", "author_1_first"=>"Luigi Piero", "author_1_last"=>"Di Bonito", "author_1_prefix"=>"", "author_1_suffix"=>"", "author_2_first"=>"Francesco Di", "author_2_last"=>"Natale", "author_2_prefix"=>"", "author_2_suffix"=>"", "author_3_first"=>"Mauro", "author_3_last"=>"Iacono", "author_3_prefix"=>"", "author_3_suffix"=>"", "title"=>"Ensemble Models for Predicting CO Concentrations: Application and Explainability in Environmental Monitoring in Campania, Italy", "year"=>"2024", "journal"=>"Proceedings - European Council for Modelling and Simulation, ECMS", "volume"=>"38", "number"=>"1", "pages"=>"558 – 564", "doi"=>"10.7148/2024-0558", "url"=>"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195192701&doi=10.7148%2f2024-0558&partnerID=40&md5=0f267aa7e7f451217cd093496cef39c5", "abstract"=>"Monitoring of non-linear phenomena, such as pollution dynamics, which is the result of several combined factors and the evolution of environmental conditions, greatly benefits by AI tools; a larger benefit derives by the application of explainable solutions, which are capable of providing elements to understand those dynamics for better informed decisions. In this paper we discuss a case with real data in which a posteriori explanations have been produced after the application of ensemble models. © ECMS Daniel Grzonka, Natalia Rylko, Grazyna Suchacka, Vladimir Mityushev (Editors) 2024.", "author_keywords"=>"Carbon monoxide; Environmental Monitoring; eX-plainalble Artificial Intelligence; Machine Learning; Pollution", "keywords"=>"Learning systems; Machine learning; Campania; CO concentrations; Ensemble models; Environmental conditions; Environmental Monitoring; Ex-plainalble artificial intelligence; Informed decision; Machine-learning; Non-linear phenomenon; Pollution dynamics; Carbon monoxide", "publication_stage"=>"Final", "source"=>"Scopus", "note"=>"Cited by: 2"}
       

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2. Applying Machine Learning to Weather and Pollution Data Analysis for a Better Management of Local Areas: The Case of Napoli, Italy

   Campanile, L., Cantiello, P., Iacono, M., Lotito, R., Marulli, F., & Mastroianni, M. (2021). Applying Machine Learning to Weather and Pollution Data Analysis for a Better Management of Local Areas: The Case of Napoli, Italy [Conference paper]. International Conference on Internet of Things, Big Data and Security, IoTBDS - Proceedings, 2021-April, 354–363. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135227609&partnerID=40&md5=5a7c117fa01d0ba8d779b0e092bc0f63

   Abstract

   Local pollution is a problem that affects urban areas and has effects on the quality of life and on health conditions. In order to not develop strict measures and to better manage territories, the national authorities have applied a vast range of predictive models. Actually, the application of machine learning has been studied in the last decades in various cases with various declination to simplify this problem. In this paper, we apply a regression-based analysis technique to a dataset containing official historical local pollution and weather data to look for criteria that allow forecasting critical conditions. The methods was applied to the case study of Napoli, Italy, where the local environmental protection agency manages a set of fixed monitoring stations where both chemical and meteorological data are recorded. The joining of the two raw dataset was overcome by the use of a maximum inclusion strategy as performing the joining action with”outer” mode. Among the four different regression models applied, namely the Linear Regression Model calculated with Ordinary Least Square (LN-OLS), the Ridge regression Model (Ridge), the Lasso Model (Lasso) and Supervised Nearest Neighbors Regression (KNN), the Ridge regression model was found to better perform with an R2 (Coefficient of Determination) value equal to 0.77 and low value for both MAE (Mean Absolute Error) and MSE (Mean Squared Error), equal to 0.12 and 0.04 respectively. © 2021 by SCITEPRESS - Science and Technology Publications, Lda.

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   {"key"=>"Campanile2021354", "type"=>"Conference paper", "bibtex"=>"@conference{Campanile2021354,\n  author = {Campanile, Lelio and Cantiello, Pasquale and Iacono, Mauro and Lotito, Roberta and Marulli, Fiammetta and Mastroianni, Michele},\n  title = {Applying Machine Learning to Weather and Pollution Data Analysis for a Better Management of Local Areas: The Case of Napoli, Italy},\n  year = {2021},\n  journal = {International Conference on Internet of Things, Big Data and Security, IoTBDS - Proceedings},\n  volume = {2021-April},\n  pages = {354 – 363}\n}\n", "author"=>"Campanile, Lelio and Cantiello, Pasquale and Iacono, Mauro and Lotito, Roberta and Marulli, Fiammetta and Mastroianni, Michele", "author_array"=>[{"first"=>"Lelio", "last"=>"Campanile", "prefix"=>"", "suffix"=>""}, {"first"=>"Pasquale", "last"=>"Cantiello", "prefix"=>"", "suffix"=>""}, {"first"=>"Mauro", "last"=>"Iacono", "prefix"=>"", "suffix"=>""}, {"first"=>"Roberta", "last"=>"Lotito", "prefix"=>"", "suffix"=>""}, {"first"=>"Fiammetta", "last"=>"Marulli", "prefix"=>"", "suffix"=>""}, {"first"=>"Michele", "last"=>"Mastroianni", "prefix"=>"", "suffix"=>""}], "author_0_first"=>"Lelio", "author_0_last"=>"Campanile", "author_0_prefix"=>"", "author_0_suffix"=>"", "author_1_first"=>"Pasquale", "author_1_last"=>"Cantiello", "author_1_prefix"=>"", "author_1_suffix"=>"", "author_2_first"=>"Mauro", "author_2_last"=>"Iacono", "author_2_prefix"=>"", "author_2_suffix"=>"", "author_3_first"=>"Roberta", "author_3_last"=>"Lotito", "author_3_prefix"=>"", "author_3_suffix"=>"", "author_4_first"=>"Fiammetta", "author_4_last"=>"Marulli", "author_4_prefix"=>"", "author_4_suffix"=>"", "author_5_first"=>"Michele", "author_5_last"=>"Mastroianni", "author_5_prefix"=>"", "author_5_suffix"=>"", "title"=>"Applying Machine Learning to Weather and Pollution Data Analysis for a Better Management of Local Areas: The Case of Napoli, Italy", "year"=>"2021", "journal"=>"International Conference on Internet of Things, Big Data and Security, IoTBDS - Proceedings", "volume"=>"2021-April", "pages"=>"354 – 363", "url"=>"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135227609&partnerID=40&md5=5a7c117fa01d0ba8d779b0e092bc0f63", "abstract"=>"Local pollution is a problem that affects urban areas and has effects on the quality of life and on health conditions. In order to not develop strict measures and to better manage territories, the national authorities have applied a vast range of predictive models. Actually, the application of machine learning has been studied in the last decades in various cases with various declination to simplify this problem. In this paper, we apply a regression-based analysis technique to a dataset containing official historical local pollution and weather data to look for criteria that allow forecasting critical conditions. The methods was applied to the case study of Napoli, Italy, where the local environmental protection agency manages a set of fixed monitoring stations where both chemical and meteorological data are recorded. The joining of the two raw dataset was overcome by the use of a maximum inclusion strategy as performing the joining action with”outer” mode. Among the four different regression models applied, namely the Linear Regression Model calculated with Ordinary Least Square (LN-OLS), the Ridge regression Model (Ridge), the Lasso Model (Lasso) and Supervised Nearest Neighbors Regression (KNN), the Ridge regression model was found to better perform with an R2 (Coefficient of Determination) value equal to 0.77 and low value for both MAE (Mean Absolute Error) and MSE (Mean Squared Error), equal to 0.12 and 0.04 respectively. © 2021 by SCITEPRESS - Science and Technology Publications, Lda.", "author_keywords"=>"Air Quality; Campania; Data Analysis; Forecasting; Machine Learning; Regression", "keywords"=>"Chemical analysis; Data handling; Environmental Protection Agency; Forecasting; Information analysis; Joining; Machine learning; Mean square error; Meteorology; Nearest neighbor search; Quality control; Campania; Health condition; Local areas; Local pollutions; Machine-learning; Quality of life; Regression; Regression modelling; Ridge regression; Urban areas; Air quality", "publication_stage"=>"Final", "source"=>"Scopus", "note"=>"Cited by: 8"}
       

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