Recently updated on July 2nd, 2024 at 02:31 pm<\/p>","protected":false},"excerpt":{"rendered":"
B-AMA is a simple, flexible protocol entirely programmed in Python that allows the application of machine learning techniques in hydrological and energy fields even to less experienced users, including all fundamental methodological steps such as data splitting, forcing selection, hyperparameter optimization, and performance evaluation.<\/p>\n","protected":false},"author":93,"featured_media":0,"comment_status":"open","ping_status":"closed","template":"","meta":{"_acf_changed":false,"footnotes":""},"tags":[1308],"targets":[1317],"pubblicazioni_tipologie":[773],"class_list":["post-188791","pubblicazioni","type-pubblicazioni","status-publish","hentry","tag-hydroelectric-en","targets-research","pubblicazioni_tipologie-isi-article-en"],"acf":{"dont_show_hompage":true,"projects":{"ID":188408,"post_author":"93","post_date":"2024-06-13 15:10:18","post_date_gmt":"2024-06-13 13:10:18","post_content":"","post_title":"Integration of renewable energy into the land and environment","post_excerpt":"The goal of the project is to develop methodologies and analyses that, starting from the characterization of technologies and with an integrated approach, provide support for energy planning, taking into account technical, economic, environmental and regulatory aspects and possible hybridization and integration.","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"integration-of-renewable-energy-into-the-land-and-environment","to_ping":"","pinged":"","post_modified":"2024-07-12 11:06:31","post_modified_gmt":"2024-07-12 09:06:31","post_content_filtered":"","post_parent":0,"guid":"https:\/\/www.rse-web.it\/progetti\/energy-from-renewable-sources-and-integration-in-the-territory\/","menu_order":0,"post_type":"progetti","post_mime_type":"","comment_count":"0","filter":"raw"},"order_posts":"","dont_show_search":false,"related_posts":false,"show_on_slider":false,"single_post_data":{"titolo_spot":"","post_content":"
In this manuscript, we present B-AMA (Basic dAta-driven Models for All), a simple, flexible, and entirely Python-based protocol for applying data-driven models (DDMs) in hydrology. The protocol, which is open-source and freely available for academic and non-commercial purposes, has been designed to enable early-career scientists with a basic programming background to develop DDMs while ensuring no steps are overlooked during implementation.<\/p>\n
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B-AMA incorporates data splitting, forcing selection, hyperparameter optimization, and performance metric computation. A Jupyter notebook with a practical workflow is provided to guide users in utilizing the protocol, while visualization tools facilitate efficient investigation and communication of results. We tested B-AMA in four hydrological applications to explore the applicability of DDMs across different temporal resolutions, lengths of time series, and autocorrelations. B-AMA demonstrated high accuracy and reasonable computational times, making the protocol ideal for educational purposes and for developing data-driven forecasts of hydrological time series.<\/p>\n","scarica_file":false,"link_estreno":[{"link_text":"Download Publication","link":"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1364815222003097?via%3dihub"}],"button":{"text":"","link":""},"referente_group":false,"data_emissione":"2022-12-13","autori":"A. Amaranto (RSE S.p.A.), M. Mazzoleni (Institute for Environmental Studies)","destinazione":"Environmental Modeling & Software, Vol. 160, No 105609","rif_rse":"22009227"},"satellite_post_url":""},"yoast_head":"\n