Abstract: A lightning damage prediction apparatus that predicts a risk of a failure of access equipment due to a lightning strike is provided. A regression equation representing a relationship between a lightning strike density for each section in a prediction target area, an equipment density for each section of components of a plurality of types constituting equipment, and a lightning damage failure density for each section of the equipment or the component, corresponding to a first period in the past, is generated, and a lightning damage failure density for each section of a prediction target area corresponding to a second period is predicted on the basis of a lightning strike density and an equipment density for each section in the prediction target area corresponding to the second period that is a prediction target, and the regression equation.

Abstract: Outdoor lightning countermeasure requiring facilities are effectively predicted. A facility information analysis unit 121 reads lightning strike information, lightning damage failure information on facilities when lightning strikes, and facility information from a recording unit 110 to extract facility information on facilities in which a lightning damage failure has occurred and facility information on facilities in which a lightning damage failure has not occurred. A parameter analysis unit 122 parameterizes items associated with the spots of the extracted facility information in which a lightning damage failure has occurred. A prediction model construction unit 131 performs the machine learning of the parameterized facility information to generate a prediction model that outputs the possibilities of the occurrence of a lightning damage failure in facilities when lightning strikes.

Abstract: A lightning damage prediction apparatus that predicts a risk of a failure of access equipment due to a lightning strike is provided. A regression equation representing a relationship between a lightning strike density for each section in a prediction target area, an equipment density for each section of components of a plurality of types constituting equipment, and a lightning damage failure density for each section of the equipment or the component, corresponding to a first period in the past, is generated, and a lightning damage failure density for each section of a prediction target area corresponding to a second period is predicted on the basis of a lightning strike density and an equipment density for each section in the prediction target area corresponding to the second period that is a prediction target, and the regression equation.