Development of a Mobile Application for the Assessment and Visualization of Recreational Quality in India’s Coastal Zones

Abstract

Beach destinations are a popular choice for travelers seeking relaxation, adventure, and scenic beauty. However, selecting the right beach location involves evaluating multiple factors, including climate conditions, humidity levels, and accommodation availability. This study aims to develop a systematic approach to finding optimal beach locations by analyzing real-time weather data such as temperature,humidity, wind speed, and seasonal variations. By leveraging thisdata, travelers can makeinformed decisions based on their comfortpreferencesandidealweatherconditions. Additionally, this study integrates hotel recommendations to enhance travel planning. Factors such ashotel proximityto the beach, user ratings, amenities, and pricing are considered to provide the best accommodation options. The combination of climate analysis and hotel recommendations ensures aseamless travel experience, enabling users to choose the most suitable beach destinations based on their specific needs. This approach enhances trip planning efficiency and helps travelers make well-informed choices, leading to a more enjoyable and comfortable beach vacation.Byenhancingthe decision-making process for travelers, thismobile application will revolutionize beach tourism management, ensuring a safer, more informed, and enjoyableexperience. Theproject contributes tothedigital transformation of India\'s tourism sector, bridging the gap between tourists, local authorities, and environmental agencies while fostering sustainable coastal tourism.

Introduction

The project focuses on developing a mobile application to guide users in selecting ideal beach destinations across India. It considers weather conditions, water quality, safety, pollution levels, and hotel proximity to provide real-time, personalized beach suitability information. Key features include:

• AI-driven Beach Suitability Index (BSI) based on weather, crowd density, pollution, and user reviews.

• GPS mapping, user ratings, and recommendations for eco-friendly tourism.

• Integration of APIs for live weather, tide, and pollution data.

• Crowdsourced feedback and AI-based image recognition to evaluate beach conditions.

Literature Review Highlights

1. Blood Donor Management System – Emphasizes user data security and geolocation-based services.

2. Online Appointment System – Focuses on real-time booking and intuitive UI/UX, relevant for the proposed app's user interaction.

3. GIS in Coastal Tourism – Demonstrates the utility of GIS for mapping and planning in coastal tourism.

4. AI for Beach Suitability Prediction – Shows the effectiveness of AI/ML in predicting beach conditions using environmental and historical data.

Methodology

• Architecture: Three-tier structure (Front-end UI, Back-end database, Data Integration layer).

• Data Sources: Weather APIs (e.g., IMD), pollution boards, tide forecasts, and user submissions.

• AI/ML Model: Uses algorithms like Random Forest to score beaches based on environmental and comfort factors.

• GIS Mapping: To display beach data visually and interactively.

Evaluation Results

• Performance: Responsive data updates with <3 sec latency; stable under 10,000 concurrent users.

• User Feedback: 90% found the app easy to use; 87% said it improved decision-making.

• Data Accuracy: 92% alignment with IMD weather reports; 88% with official pollution data.

Conclusion

The mobile application for providing recreationalsuitabilityinformation for beach locations across Indiahas proven to be an innovative and efficient solution for enhancing beach tourism experiences. By integrating real- time environmental data, AI-driven suitability scoring, crowdsourced reviews, and interactive GIS mapping, the application successfully assists tourists, environmental researchers, and local authorities in making informed decisions regarding beach visits.

References

[1] Kumar, R., & Sharma, P. (2020). GIS-Based Coastal Tourism Management: A Case Study of Indian Beaches. InternationalJournalof TourismResearch, 22(3),245-260. [2] Gupta, S., & Mehta, A. (2021). The Role of Artificial IntelligenceinEnhancingSmart Tourism:A StudyonIndian Coastal Destinations. Journal of Smart Tourism and Hospitality, 10(4), 189-205. [3] Mishra,H.,&Reddy,K.(2019). InternetofThings(IoT) for Smart Beach Monitoring and Safety. Journal of Environmental Monitoring, 15(2), 112-128. [4] Smith,J., & Brown, T. (2020). The Impact of CrowdsourcingonBeachTourismManagement:AReview of Mobile Applications.Tourism and Technology Review, 8(1), 67-80. [5] Patel, V., & Srinivasan, R. (2021). A Review on Smart Tourism and Mobile Application Integration in India. Journal of Digital Tourism Innovations, 9(2), 34-52. [6] European Environment Agency (EEA). Impact of Climate Change on Coastal Areas and Sustainable Tourism Development.(2022) [7] Das, A., & Bhowmick, S. (2022). A Machine Learning Approach for Predicting Beach Suitability Based on Environmental Parameters. International Conference on Computational Intelligence, 14(1), 98-112. [8] Center for Responsible Tourism (CRT), India. Best Practices for Sustainable Beach Management in India.(2022). [9] Sharma, P., & Verma, K. (2023). User Experience and Smart Application Development for Coastal Tourism. Journal of Human-Centered Computing, 12(3), 177-192. [10] Jain, M., & Kulkarni, A. (2023). Smart Cities and SustainableBeachTourism:IntegratingIoTandAISolutions. International Journal of Smart Tourism and Urban Development, 11(2), 88-105.

Copyright

Copyright © 2025 A Srinadh Reddy, Laksmi Narayana Reddy, Kakumuri Poojesh, S R Ramya, Kavitha V. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.