Design of Flood Early Detection Based on the Internet of Things and Decision Support System
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Flooding is a natural disaster that has a serious impact on humans, the environment, and the economy.To reduce the risk and adverse impacts of flooding, this research aims to design an Internet of Things (IoT) based early detection system integrated with a decision support system.The proposed system uses various types of sensors, such as DHT22 to monitor air temperature and humidity, an Ombrometer to measure rainfall, a Water Flow Sensor to measure water flow, and an Ultrasonic Sensor to detect changes in water level.Data from these sensors will be collected in real time and analyzed to predict potential flooding.In addition, the system will have a user interface that facilitates monitoring and decisionmaking by authorities.The decision support system will use sensor data and weather information to warn decision-makers early of potential flooding and appropriate action recommendations.This research is expected to improve the ability to detect and respond to floods more effectively, thereby assisting in protecting human lives, protecting the environment, and reducing the economic impact of floods.In addition, this research contributes to the development of IoT-based technologies and decision support systems in the context of natural disaster mitigation.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it