Open AccessTwo-stage incidents prediction in heat supply systems using gradient boosting
Author(s) -
A A Akhvaev,
Valeriy Fedorovich Shurshev,
N A Nosikovsky
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2060/1/012011
Subject(s) - boosting (machine learning) , gradient boosting , computer science , risk analysis (engineering) , operations research , artificial intelligence , business , engineering , random forest
Information technologies usage in heat supply sphere has an important role to solve accidents problems in heat networks. Heat networks are accidents (incidents) sources. Accident situation predicting in heat supply systems will make it possible to rationally finance incidents prevention funds. AI is the tool for predicting accidents. Modern AI methods and algorithms allow to solve many technical systems problems. Research area has a data bank that allows to forecast heat networks accidents. Gradient boosting and production rules were chosen as basic methods.