Sumit Shekhar Reviewer
Approved
1. Relevance and Originality
The paper makes a strong and timely contribution by addressing climate vulnerability assessment through the lens of edge AI integrated IoT systems. Given the increasing frequency and intensity of climate related hazards, the focus on operational resilience and real time decision support is highly relevant. The originality of the work lies in its holistic framing, which bridges climate science, infrastructure engineering, edge computing, and governance. This broad perspective is particularly valuable for decision makers and practitioners seeking to translate research advances into deployable solutions.
2. Methodology
The paper adopts a clear and well reasoned survey approach, with carefully defined scope boundaries and a logical progression from conceptual foundations to applied domains. The classification of technologies and methods is systematic and easy to follow. While the work does not aim to be a formal systematic review, briefly clarifying whether the literature coverage is intended to be comprehensive or representative would help manage reader expectations. Overall, the methodological organization supports the paper’s goal of synthesis and guidance.
3. Validity and Reliability
The analysis is firmly grounded in reputable sources, including international climate assessments, recognized technical standards, and recent peer reviewed studies. This strong evidentiary base enhances confidence in the conclusions. The attention given to uncertainty, calibration, and governance demonstrates a mature understanding of real world deployment risks. Reliability could be further reinforced by explicitly highlighting which application examples reflect long term operational use versus pilot or experimental deployments.
4. Clarity and Structure
The manuscript is clearly written and maintains a consistent narrative throughout. Complex concepts are explained in an accessible manner without oversimplification, making the paper suitable for a multidisciplinary audience. The use of figures, tables, and highlighted notes effectively supports comprehension. Minor improvements could be achieved by tightening a few dense sections, but overall the structure successfully balances technical depth with readability.
5. Results and Analysis
The paper effectively synthesizes reported outcomes from multiple application domains, demonstrating how edge AI can reduce latency, improve continuity during disruptions, and support risk informed decision making. The discussion of evaluation metrics and decision centric benchmarks is particularly useful and forward looking. The concluding emphasis on governance, trustworthiness, and integration into digital twin driven workflows strengthens the paper’s impact and underscores its relevance for future research, policy development, and infrastructure planning.
Sumit Shekhar Reviewer