| Autor: |
Nathalia Silva-Cancino, Fernando Salazar, Ernest Bladé |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
SoftwareX, Vol 26, Iss , Pp 101657- (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2352-7110 |
| DOI: |
10.1016/j.softx.2024.101657 |
| Popis: |
Potential risk identification due to off-stream reservoir failure typically requires the use of two-dimensional hydraulic models, which demands considerable effort in terms of expertise, time and financial resources. Unfortunately, not all reservoir owners have access to these resources, and the process of assessing hazard classifications for administrations can be burdensome. ACROPOLIS was developed to address this challenge, employing a Machine Learning model to provide a preliminary risk classification according to Spanish regulations for off-stream reservoirs without the necessity of building a hydraulic model. This approach has been integrated into a user-friendly interface, simplifying the process for users. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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