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Weather decisions at a stadium require more than a generic city forecast. Event organisers need useful estimates for rain probability, temperature, humidity, wind, and heat stress at the venue, often several hours before play or a performance. Bagged trees can provide a strong baseline when you have reliable local observations and carefully designed time-based features.

This guide explains how to use bagged trees to predict weather in Pune Stadium, while keeping expectations realistic: an ensemble model can improve a site-specific forecast, but it does not replace official warnings from the India Meteorological Department (IMD) or a trained forecaster.

What bagged trees do

Bagging, short for bootstrap aggregating, trains many decision trees on bootstrapped samples of the training data and combines their outputs. For weather applications:

• Regression estimates continuous values such as temperature, wind speed, or rainfall amount.
• Classification estimates categories such as rain/no rain, heavy-rain risk, or unsafe heat conditions.
• Averaging or voting reduces the instability of a single decision tree.
• Out-of-bag evaluation offers an additional estimate of generalisation error without creating a separate validation prediction for every tree.

A random forest is a related, more randomised tree ensemble. A standard bagged-tree model is useful when you want a transparent, low-maintenance benchmark before testing more complex approaches. Teams building several forecast models can place it inside scalable ML pipelines for predictive analytics, with scheduled retraining and monitoring.

Define the Pune Stadium forecasting problem

Start by specifying the forecast horizon and decision. A model for a match starting at 7:30 pm is different from one used for a morning training session.

Useful target definitions include:

• Rain occurrence in the next 1, 3, or 6 hours.
• Rainfall accumulation in the next hour or event window.
• Temperature and relative humidity at kickoff.
• Maximum wind speed during the event.
• A practical operating label such as green, amber, or red, based on rain, lightning, heat, and wind thresholds.

Do not assume that “Pune weather” equals conditions inside the stadium. The venue’s surface, nearby buildings, elevation, drainage, and local convective storms can create differences from observations several kilometres away. Treat the stadium as a location-specific forecasting point and record the exact sensor coordinates and height.

For comparisons with other Indian deployments, Bhubaneswar weather prediction with Hugging Face models and Guwahati weather prediction with Hugging Face models illustrate why regional climate and local data coverage matter.

Collect and structure the data

A useful dataset combines venue observations with broader atmospheric context. Potential inputs include:

• Temperature, dew point, relative humidity, pressure, wind speed, wind direction, and rainfall.
• Hour, day of year, monsoon or dry-season indicator, and holiday or event schedule.
• Recent rainfall totals over 15 minutes, 1 hour, 3 hours, 6 hours, and 24 hours.
• IMD observations or forecasts, radar-derived precipitation where available, and satellite cloud products.
• Nearby station readings, with distance and elevation included as features.
• Stadium-specific information such as roof coverage, pitch status, drainage alerts, and sensor availability.

Store observations with timestamps in a consistent timezone, preferably IST, and preserve the original ingestion time. This prevents data leakage, where a feature accidentally contains information that would not have been available at prediction time. Resample inputs to a fixed interval, such as 15 or 60 minutes, and document how missing values, sensor outages, and duplicate readings are handled.

At least one full annual cycle is preferable; several years are better for rainfall variability. Pune’s monsoon behaviour can produce rare but operationally important downpours, so evaluate those events separately rather than allowing the large number of dry hours to dominate the score.

Engineer features that reflect weather dynamics

Tree models do not automatically understand time, persistence, or circular direction. Add features deliberately:

• Lagged values from the previous 1, 2, 3, 6, and 24 hours.
• Rolling means, maxima, minima, and rainfall sums.
• Sine and cosine transforms for hour of day and day of year.
• Wind direction represented as sine and cosine rather than a raw degree value.
• Differences such as pressure change and humidity change over three hours.
• Forecast-versus-observation differences from an external weather provider.
• Interaction indicators such as high humidity plus falling pressure.

For a rainfall classifier, define the label before building features. For example, rain_next_3h = 1 if measured rainfall exceeds a chosen threshold during the following three hours. Never use readings from that future window as input.

Train and validate the model correctly

Use chronological splits, not a random train-test split. A practical design is:

1. Train on earlier months or years.
2. Validate on the next block of time for feature and hyperparameter decisions.
3. Test once on the most recent untouched period.
4. Report results separately for monsoon, winter, summer, daytime, and event windows.

For regression, report MAE, RMSE, and bias. For rain classification, include precision, recall, F1 score, ROC-AUC, and especially precision-recall performance when rain events are uncommon. Calibrate probabilities so that a predicted 40% rain chance means approximately 40% occurrence over comparable cases. Also compare against simple baselines: persistence, seasonal averages, and the official forecast.

A compact Python example for current scikit-learn versions is:

from sklearn.ensemble import BaggingRegressor
from sklearn.tree import DecisionTreeRegressor
from sklearn.metrics import mean_absolute_error

model = BaggingRegressor(
    estimator=DecisionTreeRegressor(
        max_depth=12,
        min_samples_leaf=5,
        random_state=42
    ),
    n_estimators=200,
    max_samples=0.8,
    bootstrap=True,
    n_jobs=-1,
    random_state=42
)

model.fit(X_train, y_train)
predictions = model.predict(X_test)
print(mean_absolute_error(y_test, predictions))

For a rain/no-rain target, use BaggingClassifier and evaluate probability calibration. Older examples may use base_estimator; newer scikit-learn releases use estimator, so pin and record your package versions.

Turn predictions into an event workflow

A forecast is valuable only when it supports a clear action. Run the model on a fixed schedule, such as every 15 minutes, and publish:

• Forecast value and probability.
• Forecast horizon and data timestamp.
• Confidence or uncertainty range.
• Comparison with the previous run.
• Triggered action, such as inspecting drainage, delaying warm-ups, or moving equipment.

Use thresholds agreed with venue operations, not thresholds chosen solely for the best offline score. Keep an audit log of inputs, model version, output, and the action taken. This makes post-event review possible and exposes model drift.

The same monitoring discipline used in predictive maintenance solutions for Indian factories applies here: track missing sensors, stale feeds, changing error rates, and whether the model performs worse during unusual weather. A lightweight dashboard can show rolling MAE, rain-event recall, calibration, and data freshness.

Limits, safety, and next steps

Bagged trees are robust, but they cannot create information absent from the sensors. Short-lived thunderstorms, lightning, radar gaps, and abrupt wind shifts remain difficult. Do not use a model output as the sole basis for lightning or severe-weather safety decisions; follow IMD alerts and venue emergency procedures.

Improve the system by adding reliable radar or satellite features, retraining after major sensor changes, calibrating probabilities, and comparing the ensemble with gradient boosting or specialised time-series models. Explain predictions with permutation importance or local explanation tools, while checking that apparent importance is not caused by leakage.

For a small Pune venue team, the best starting point is straightforward: establish clean local measurements, build a persistence baseline, train a bagged-tree model, validate it chronologically, and connect the output to an explicit event playbook. That approach produces a measurable forecasting service rather than a model that only performs well in a notebook.