Supervised learning for maritime search operations: An artificial intelligence approach to search efficiency evaluation

Abstract

We present a metamodeling approach, based on supervised learning, to estimate the probability of success of maritime search and rescue operations. The objective is to improve search planning in a context where lives are at risk and time is of the essence. The proposed approach has been evaluated both in terms of its predictive performance (Can the probability of success be closely approximated?), and of its added value to the decision support system operationally used by the Canadian Coast Guard (To what extent does the approach improve the current system?). We conducted extensive experimentations to evaluate and compare four machine learning algorithms namely, random forest, k-nearest neighbor, support vector machine regression, and feed forward neural networks with a single layer. Our experimental results, based on real-life data, show that the learned models can approximate the probability of success with sufficient precision and that a heuristic, implementing a k-nearest neighbor model within the existing decision support system, can recommend search plans with higher probabilities of success, which has the potential to save more lives.