Kolkata’s cricket venues need more than a generic city forecast. A match-day system should estimate rainfall probability, rain intensity, temperature, humidity, wind, and the likelihood of a usable playing surface at short lead times. This guide explains how to use dilated convolutions to predict weather in Kolkata cricket stadium, while keeping the project technically sound and operationally useful.
Dilated convolutions are not a replacement for meteorological expertise or established numerical weather prediction. They are a modelling component that can help combine local sensor readings with spatial and temporal context. For a production system, compare the model with official forecasts, radar products, and simple baselines before using it to influence scheduling or ground operations.
Define the prediction task first
Start with a narrow decision rather than a vague goal such as “predict the weather”. Useful targets include:
- Probability of measurable rain in the next 15, 30, 60, and 120 minutes.
- Expected rainfall accumulation over the next hour.
- Probability of a rain interruption lasting more than 10 minutes.
- Temperature, relative humidity, wind speed, and gusts during the match window.
- A derived ground-readiness indicator, clearly labelled as an estimate rather than an official decision.
For Kolkata, short-duration convective showers, high humidity, monsoon cloud cover, and rapid local changes matter more than a broad regional average. Define the venue coordinates precisely, then create a small spatial grid around Eden Gardens or the relevant stadium. The grid should capture nearby observations without pretending that a model can resolve every pitch-level effect.
Assemble a reliable Indian weather dataset
A useful training table or tensor can combine:
- Automatic weather-station readings near the venue.
- Rain gauges and, where legally and technically available, weather-radar reflectivity or precipitation estimates.
- Satellite-derived cloud and infrared features.
- Numerical weather prediction variables such as pressure, wind fields, temperature, and precipitation.
- Calendar features, match start time, daylight, season, and monsoon phase.
- Venue observations such as drainage status or a covered/uncovered indicator, if the target is operational readiness.
Store the source, timestamp, unit, location, and quality flag for every observation. Align all data to a fixed interval, such as five or ten minutes. Do not silently forward-fill rainfall or radar values across long gaps. Add missingness indicators so the model can distinguish “no rain observed” from “no sensor reading”. Projects that will eventually serve several locations should adopt scalable ML pipelines for predictive analytics from the beginning.
Where dilated convolutions fit
A standard convolution sees neighbouring values. A dilated convolution inserts gaps between kernel elements, expanding the receptive field without proportionally increasing the number of parameters. With a one-dimensional time series, dilation rates of 1, 2, 4, and 8 can expose both recent changes and longer trends. With gridded radar or satellite data, two-dimensional dilations can capture a wider cloud or rainfall structure while preserving the grid’s resolution.
A practical architecture can use two branches:
1. A temporal branch that processes station and forecast variables with one-dimensional causal convolutions.
2. A spatial branch that processes radar, satellite, or gridded numerical-weather inputs with two-dimensional convolutions.
Fuse the branches before separate output heads predict rainfall probability, rainfall amount, and continuous weather variables. Causal padding is important when the system is evaluated as it would operate in real time; otherwise, future observations can leak into the input window.
Do not assume dilation is automatically superior. Compare it with persistence, a rolling-average model, gradient-boosted trees, LSTM or temporal-transformer baselines, and a conventional convolutional network. The same disciplined comparison used in building predictive maintenance systems with AI applies here: establish a dependable baseline, isolate the improvement, and measure failure modes.
Build the training examples
For every forecast issue time, create an input window—for example, the previous two hours of five-minute observations—and a target window covering the next two hours. Normalise continuous variables using training-set statistics only. Encode wind direction as sine and cosine rather than as degrees, and include cyclical features for hour of day and day of year.
Use a chronological split:
- Earlier periods for training.
- A later period for validation and hyperparameter selection.
- The most recent monsoon and dry-season periods for final testing.
Randomly splitting adjacent weather windows produces leakage because nearly identical conditions appear in both sets. Test separately on monsoon showers, thunderstorms, dry heat, sensor outages, and unusual events. If radar or satellite data is unavailable for some dates, evaluate the degraded model as a separate operating mode.
Train for probabilistic forecasts
Rain is a classification and forecasting problem, not merely a regression target. Use binary cross-entropy or a calibrated classification loss for rain occurrence, and a suitable regression loss—such as Huber or a weighted mean error—for rainfall amount. Because heavy rain is less frequent than no rain, report class-balanced metrics and do not optimise accuracy alone.
Track:
- Precision, recall, F1, and area under the precision-recall curve for rain events.
- Brier score and reliability diagrams for probability calibration.
- Mean absolute error for temperature, humidity, and wind.
- Quantile loss or prediction-interval coverage for uncertain rainfall totals.
- Lead-time-specific performance at 15, 30, 60, and 120 minutes.
Calibrate the final probabilities on a held-out validation period. A forecast of 70% rain should mean roughly seven comparable cases in ten, not simply represent a high neural-network score. Satellite inputs can be particularly valuable for spatial context; compare their contribution with a satellite-based yield prediction system, while recognising that the target and spatial scales are different.
Deploy for match-day decisions
A compact inference service can refresh predictions every five minutes and publish a timestamped forecast with confidence intervals. The user interface should show:
- Current observations and data freshness.
- Rain probability by lead time.
- Expected rainfall range, not a single false-precision number.
- Radar or satellite context where available.
- Model version, missing inputs, and uncertainty warnings.
Define action thresholds with ground staff and match officials. For example, a high probability of heavy rain may trigger an inspection workflow, while a moderate probability should prompt monitoring rather than an automatic decision. Keep human approval for covers, drainage, player movement, and match scheduling. Audit every forecast and outcome so the system can be recalibrated after each season.
Common failure modes
- Data leakage: using a later radar frame or revised observation in a past forecast.
- Poor spatial alignment: treating a city-wide reading as a direct measurement of the stadium.
- Overfitting: using a large model on a small number of local rain events.
- Uncalibrated probabilities: presenting model confidence as certainty.
- Sensor dependence: failing when a station, API, or satellite feed is delayed.
- Concept drift: assuming monsoon behaviour, urban development, or equipment remains unchanged.
Set up monitoring for missingness, input ranges, forecast calibration, and performance by season. A resilient service should fall back to an ensemble of simpler models and official sources rather than stop producing information. Teams building broader monitoring practices can borrow ideas from AI predictive maintenance for railway infrastructure assets, where alert quality and operational reliability matter as much as model accuracy.
A sensible 2026 implementation plan
Begin with a 15-minute rain-occurrence baseline using station data and persistence. Add temporal dilated convolutions, then introduce gridded radar or satellite inputs only after data quality is stable. Run a complete monsoon backtest, publish calibration results, and conduct a shadow deployment before allowing the forecast to inform venue workflows.
The strongest system will not be the most complex one. It will be the one that gives Kolkata’s ground and match teams a timely, calibrated forecast, explains when its inputs are weak, and improves through measured seasonal evaluation. For founders building such systems in India, AI Grants India can help identify relevant support and funding pathways.