Intelligent Sea States Identification Based on Maximum Likelihood Evidential Reasoning Rule

Round 1

Reviewer 1 Report

The authors developed a reasoning model based on maximum likelihood evidential reasoning (MAKER) rule to identify the propeller ventilation type, and the result is used as the basis for the sea states identification. The genetic algorithm is used to optimize the parameters of MAKER model to improve the evaluation accuracy. The authors have conducted simulation data to train the MAKER model to evaluate their proposed approach. The problem addressed by the authors is interesting and the paper is overall well written. However, I have the following suggestions:

1) Some of the references are too old. Please update the latest article in five years as a reference.

2) More work is needed for the conclusion section in terms of the significance and applicability of the proposed approach. Also, the authors could give specific details about future work.

Author Response

Please see the attachment.

Author Response File: Author Response.docx

Reviewer 2 Report

The paper proposes an intelligent sea states identification model based on MAKER rule to identify the current sea state effectively and choose the suitable control strategy for propulsion system.

The submission addresses a very interesting research topic, which is relevant for the journal.

The idea is well described through a rigorous mathematic formulation.

The paper has the potentiality to give a good contribute to the research.

Nevertheless, I suggest to improve the introduction and conclusions Sections.

1. In the introduction the benefits of using GA and more in general the machine learning approach in addressing numerous problems belonging to different fields should be reported. In this direction, I suggest to mention the following papers:

"Toward a soft computing-based correlation between oxygen toxicity seizures and hyperoxic hyperpnea",              Soft Computing 22(7), pp. 2421-2427. doi: 10.1007/s00500-017-2512-z

"A proposal for distinguishing between bacterial and viral meningitis using genetic programming and decision trees", Soft Computing, 23(22), pp. 11775-11791. DOI: 10.1007/s00500-018-03729-y

2. In the conclusions section, some detailed discussion about the impact of the results in real scenarios should be added.
3. Moreover, future works are missing.
4. There are typos that need to be fixed.
5. As argued by authors, the SVM results are similar to that obtained by the proposal making use of MAKER model. Thus, the benefits of the proposal should better illustrated.

Author Response

Please see the attachment.

Author Response File: Author Response.docx

Round 2

Reviewer 2 Report

The authors have successfully addressed any my question and doubt. Nevertheless, I noted error in the new added references. It is only reported the first name and not the last-name. For example: Gianni, D.A. is incorrect. The correct author's name is D'Angelo, G.

Apart this, the paper is ready to be pubblished.