Geo-spatial intelligence for searching and rescuing man overboard incidents using an artificial neural network: An empirical study of the Royal Thai Navy in the Gulf of Thailand

Man overboard (MOB) incidents happened continuously and they are the main type of suffering incident at the sea. From the MOB situation, searching and rescuing (SAR) operations are occurring in global coastal countries. According to the literature reviews on SAR operations, most studies presented the methodology to search the MOB in better performance indicators after the MOB occurred however, no evidence has been found on using Geo-spatial intelligence with Artificial Neural Networks (ANN) to predict MOB incidents under various weather conditions before the MOB occurred. The objective of this research, therefore, is to propose an intelligent methodology to reduce the searching and rescuing operation time of MOB incidents. A model of Artificial Neural Network (ANN) is applied due to its ability to accurately predict location and practicality of the Royal Thai Navy (RTN). The MOB incidents from 2019 to 2022 are collected and generated for the methodology implementation. This research utilizes the advantage of the Back-Propagation Artificial Neural Network (BP-ANN) to apply in prediction model in which the input variables are various weather condition information including the location of MOB from Geo-spatial, and the output variables are MOB location in the form of Latitude and Longitude. The appropriate BP-ANN model is created under the trial and error method, and the results show that the BP-ANN can predict the location of the MOB accurately. The benefit of this research is to reduce the time consumed by SAR operations in the Gulf of Thailand. As a result, when the traditional SAR operation time is compared with the SAR operation time by using BP-ANN predicting model, it was found that the SAR operation time by using BP-ANN predicting model is more accurate and can reduce the operation time by 5 hrs.