What a TCN can—and cannot—predict at Chepauk
The MA Chidambaram Stadium, commonly called Chepauk, sits close to Chennai’s coast. That location creates fast-changing humidity, sea-breeze effects, intense heat, and occasional sharp rain events. A Temporal Convolutional Network (TCN) can learn patterns in these sequential observations and produce short-horizon forecasts for event operations.
Use the model for decisions such as:
- Probability of rain in the next 15, 30, 60, or 180 minutes
- Temperature and relative-humidity forecasts
- Wind speed and direction changes
- Heat-stress indicators for players, staff, and spectators
- A rolling “weather disruption” risk score
A TCN should complement—not replace—official warnings and nowcasts from the India Meteorological Department. For lightning, extreme rainfall, or cyclone-related conditions, safety decisions must follow competent authorities and venue protocols.
Define the forecast before collecting data
Start with a precise target. “Predict the weather” is too broad for a useful system. For a cricket venue, a sensible first version is a multi-output forecast every 5 or 15 minutes:
- Rain occurrence in the next 30 minutes: binary classification
- Rainfall amount over the next hour: regression
- Temperature and humidity at the next hour: regression
- Wind speed and gust risk: regression or classification
Create separate models or prediction heads for each horizon if performance differs materially. Short-horizon precipitation is usually harder than temperature because convective showers can develop quickly and may be poorly represented by a single nearby station.
Set an operational threshold with venue teams. For example, an alert might trigger when the probability of measurable rain exceeds 60%, while a lightning alert may require a lower threshold because the cost of missing the event is high. Record these decisions before testing the model to avoid tuning the system to historical outcomes.
Build a Chennai-specific dataset
Use timestamped observations from multiple sources rather than relying on one consumer weather app. Useful inputs include:
- IMD station observations and forecasts, where available
- A calibrated weather station at or near the stadium
- Rain gauges and nearby stations across central Chennai
- Radar-derived rainfall or precipitation estimates
- Numerical weather prediction variables
- Venue context such as match start time, roof or cover status, and scheduled breaks
Core features should include temperature, dew point, relative humidity, pressure, rainfall intensity, wind speed, wind direction, cloud cover, solar radiation, and visibility. Derive rolling rainfall totals over 10 minutes, 30 minutes, 1 hour, and 3 hours. Encode wind direction as sine and cosine rather than as a raw degree value, since 359° and 1° are nearly identical.
Data quality is often more important than network depth. Align all feeds to a common time zone—preferably Asia/Kolkata—deduplicate timestamps, flag sensor outages, and retain missingness indicators. Do not silently forward-fill rainfall or gust measurements across long gaps. For a broader production approach, the principles in implementing scalable ML pipelines for predictive analytics are directly applicable.
Prepare leakage-safe training windows
A TCN receives a fixed sequence of historical observations. If readings arrive every 10 minutes, a 12-hour lookback contains 72 time steps. Create samples such as:
- Input: the previous 72 time steps
- Target: rainfall occurrence during the next six time steps
- Optional targets: temperature, humidity, and wind at the forecast horizon
Split the data chronologically, not randomly. Train on earlier periods, validate on later periods, and reserve the most recent monsoon and non-monsoon periods for testing. A random split can place adjacent observations in both training and test sets, producing an unrealistically strong score.
Fit scaling parameters only on the training set. For imbalanced rain classification, use class-weighted binary cross-entropy or focal loss, and report precision, recall, F1, and area under the precision-recall curve. For continuous targets, use MAE, RMSE, and forecast bias. Compare the TCN with persistence, climatology, logistic regression, and gradient-boosted trees. A complex model is worthwhile only if it beats these baselines in the conditions that matter operationally.
Design a practical TCN
A TCN typically uses causal, dilated one-dimensional convolutions. Causal padding prevents future information from entering the input representation, while dilation lets the model capture patterns over several hours without making the network excessively deep. Residual blocks help optimisation.
A sensible starting configuration is:
- Two to four residual blocks
- Kernel size of 3 or 5
- Dilation factors of 1, 2, 4, 8, and possibly 16
- 32–128 filters per block
- Dropout between 0.05 and 0.2
- A sigmoid output for rain probability
- Linear outputs for temperature, humidity, or rainfall amount
Use a multi-task model only after verifying that the targets benefit from shared representations. Otherwise, separate heads can obscure which output is failing. AdamW, early stopping, and learning-rate reduction are reasonable defaults. Keep the first version small enough to retrain quickly on a CPU or modest cloud instance.
For an implementation pattern, use TensorFlow/Keras or PyTorch and make the receptive field explicit. If the receptive field is shorter than the lookback window, the additional history contributes little. Save the feature schema, scaler, model version, training period, and data-source metadata with every release.
Evaluate for real stadium decisions
Aggregate metrics can hide the failures that matter. Evaluate separately for:
- Southwest and northeast monsoon periods
- Day and night matches
- Dry heat, high-humidity, and active-rain conditions
- Precipitation onset versus precipitation continuation
- Lead times of 15, 30, 60, and 180 minutes
Calibrate probabilities with reliability curves or isotonic regression. A forecast labelled 70% should correspond to rain roughly 70% of the time in comparable cases. Provide prediction intervals for temperature and rainfall rather than a single number. Operators need both the forecast and its confidence.
Track drift after deployment. Sensor relocation, construction, blocked instruments, or a changed data provider can degrade accuracy without any code change. The monitoring practices used in building predictive maintenance systems with AI—alerting on data quality, distribution shifts, and service failures—translate well to weather systems.
Deploy an alerting workflow
A useful stadium system is a small decision service, not just a notebook. Ingest observations, validate the latest window, run inference, store predictions, and expose results through a dashboard or operations channel. Show:
- Current observations and their age
- Rain probability by horizon
- Expected rainfall range
- Wind and lightning-related signals
- Model confidence and data-quality status
- Last successful forecast time
Use hysteresis so an alert does not switch on and off with every update. For example, activate a rain-watch alert above one threshold and clear it only after the probability remains below a lower threshold for two consecutive runs. Keep a human approval step for public announcements, pitch-cover decisions, and match scheduling.
Common mistakes to avoid
- Training on weather-app forecasts while claiming to predict from observations
- Mixing future radar or revised observations into historical input windows
- Using only one station for a coastal, convective environment
- Reporting accuracy without a persistence baseline
- Optimising RMSE while ignoring missed rain events
- Treating model output as an official safety warning
- Deploying without fallback logic when data feeds fail
A robust fallback can display the latest verified observation, official IMD guidance, and a clear “forecast unavailable” status. Never substitute stale model output for current safety information.
A sensible build plan for 2026
Begin with a 15-minute pipeline, a 6–12-hour lookback, and one high-value target: rain probability for the next 30 minutes. Establish data validation and baseline performance before adding radar, satellite, or multi-task outputs. Once the model proves useful across seasons, add probabilistic calibration, drift monitoring, and a lightweight dashboard.
The same disciplined approach used in Bhubaneswar weather prediction with Hugging Face models and Guwahati weather prediction with Hugging Face models can help compare model choices across Indian climates. For Chepauk, however, local sensor quality, coastal feature engineering, and operational thresholds will matter more than choosing the newest architecture.