A method for condition prediction of mooring lines of a floating system , the method comprising: providing simulation data to the neural network model , the simulation data including at least one simulated value of a mean floating system offset and at least one simulated floating system long drift period corresponding to the at least one simulated value of the floating system offset; providing decision logic to the neural network model to process the simulation data for a neural network model output; obtaining real world data including at least one real world value of the mean floating system offset and at least one real world floating system long drift period corresponding to the at least one real world value of the mean floating system offset; comparing the real world data with the simulation data to determine a metric of similarity between the real world data and the simulation data; providing the real world data as operational data for the neural network model if the metric of similarity is simulation data greater than a threshold metric to produce an output corresponding to an output with the simulation data from the neural network model ; adjusting the real world data to correspond with the simulation data and providing the adjusted data as the operational data for the neural network model if the metric is not greater than the threshold metric; and operating the neural network model with the adjusted data that corresponds at least within the range of the simulation data to produce an output with the adjusted data corresponding to an output with the simulation data from the neural network model , wherein the output variables are damage indicators of each mooring line of the floating system . providing simulation data to the neural network model , the simulation data including at least one simulated value of a mean floating system offset and at least one simulated floating system long drift period corresponding to the at least one simulated value of the floating system offset; providing decision logic to the neural network model to process the simulation data for a neural network model output; obtaining real world data including at least one real world value of the mean floating system offset and at least one real world floating system long drift period corresponding to the at least one real world value of the mean floating system offset; comparing the real world data with the simulation data to determine a metric of similarity between the real world data and the simulation data; providing the real world data as operational data for the neural network model if the metric of similarity is simulation data greater than a threshold metric to produce an output corresponding to an output with the simulation data from the neural network model ; adjusting the real world data to correspond with the simulation data and providing the adjusted data as the operational data for the neural network model if the metric is not greater than the threshold metric; and operating the neural network model with the adjusted data that corresponds at least within the range of the simulation data to produce an output with the adjusted data corresponding to an output with the simulation data from the neural network model , wherein the output variables are damage indicators of each mooring line of the floating system . <EOS>