AI-Driven Water Resource Management in Tourism-Intensive Regions: A Smart Sustainability Model
Bibliographic record
Abstract
Tourism-intensive regions are increasingly vulnerable to water stress due to high seasonal demand, climate variability, and limited water infrastructure. To address these challenges, this paper introduces a novel AI-driven framework for real-time monitoring, predictive analytics, and adaptive management of water resources in hospitality sectors such as hotels, resorts, and tourism hubs. Leveraging Internet of Things (IoT) sensors, cloud-based data systems, and machine learning algorithms, the model ensures efficient water consumption, anomaly detection, and data-driven decision-making to support operational sustainability. The proposed system incorporates dynamic forecasting of water usage patterns, integrates occupancy and environmental data, and enables automated adjustments in water delivery systems. It facilitates stakeholder collaboration through transparent dashboards and alerts that guide facility managers in adopting responsible water practices. This model not only enhances operational efficiency but also empowers tourism enterprises to align with the United Nations Sustainable Development Goals (SDG 6: Clean Water and Sanitation and SDG 12: Responsible Consumption and Production). By minimizing water waste and promoting equitable resource allocation, the framework supports resilience in water-scarce regions heavily reliant on tourism revenues. Case study simulations in coastal and island tourism zones demonstrate the framework’s potential to reduce water usage by up to 30% annually, while also improving guest satisfaction through sustainable service delivery. Additionally, the system’s modular design ensures scalability and adaptability across diverse geographies and hotel classes. The paper underscores the role of climate-smart tourism infrastructure in mitigating environmental impact while enhancing economic viability. This initiative reflects our leadership in sustainable development and innovation at the intersection of artificial intelligence and tourism management. It pioneers a path forward for eco-conscious travel, integrating technological advancement with stewardship of natural resources. The AI-driven model presents a transformative approach to balancing growth in the tourism sector with the imperative of environmental sustainability, offering a replicable blueprint for global adoption.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".