Design and Implementation of a Multi-Tier Scheduling Framework for Real-Time Urban Water Logging Detection and Dispatch Optimization
Bibliographic record
Abstract
Urban waterlogging has escalated into a chronic and debilitating crisis across India, inflicting severe economic, infrastructural, and public health consequences.This systemic failure of modern urban water management stands in stark contrast to the sophisticated and resilient hydraulic engineering of the ancient Indus Valley Civilization.This paper introduces a novel Multi-Tier Scheduling Framework designed to address this contemporary challenge by drawing inspiration from ancient design philosophies while leveraging state-of-the-art technology.The framework employs a three-tier architecture-Perception, Fog, and Cloud-that facilitates real-time waterlogging detection, predictive analysis, and optimized emergency resource dispatch.The Perception Tier integrates a dense network of low-cost IoT sensors (ultrasonic and pressure) and fuses this quantitative data with qualitative insights derived from Natural Language Processing (NLP) of social media feeds and meteorological forecasts.The Fog Tier, operating at the network edge, utilizes a hybrid Transformer-Long Short-Term Memory (LSTM) deep learning model for low-latency, localized waterlogging prediction.The Cloud Tier orchestrates city-wide response, employing a metaheuristic optimizer based on a hybrid Ant Colony Optimization and Genetic Algorithm (ACO-GA) to solve the dynamic vehicle routing problem for emergency dispatch.A preemptive, priority-based real-time scheduler governs the entire framework, ensuring that time-critical tasks are prioritized during emergencies.A simulated implementation using geospatial and hydrological data from a flood-prone urban zone demonstrates the framework's efficacy.The results indicate a significant improvement in prediction accuracy and a substantial reduction in emergency response times compared to baseline models.This research presents a holistic, technologically advanced, and historically informed blueprint for building climate-resilient and intelligent urban water management systems in India and beyond.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 0.001 |
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".