A Probabilistic Markov Chain Model for Short-term Water Demand Forecasting
Bibliographic record
Abstract
Urban water management remains a crucial concern for city managers and planners. As water demand forecasting plays a key role in urban water management, identifying factors influencing water demand is particularly important to mitigate water shortage crises. This study utilizes a Markov chain model and Artificial Neural Networks (ANNs) to estimate short-term urban water demand in Tehran. The variables considered for estimation include maximum temperature, water consumption, and precipitation rate in the previous four days. These variables are used as previous events to predict water consumption on the fifth day. Daily data from March 21, 2018 to March 19, 2021 were collected for analysis. The results of the study indicate that the Markov model's forecasting is more accurate compared to the ANN model. The Markov chain model demonstrated 48% and 65% improvement in accuracy compared to the ANN model for the test data and the training data, respectively. This suggests that a Markov chain model can be a valuable tool for estimating short-term urban water demand. The findings of this study can contribute to better urban water management and planning to address water shortage issues effectively.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".