Weather forecasting for a cricket venue is not simply a matter of predicting tomorrow’s temperature. At Bengaluru’s M. Chinnaswamy Stadium, a useful model should estimate conditions that affect play: rainfall probability, rain intensity, temperature, humidity, wind, visibility, and the likelihood of a delayed or interrupted match. The right approach combines local observations, reliable time-series design, and evaluation that reflects match-day decisions.
This guide explains how to use LSTM models to predict weather in M Chinnaswamy Stadium with a reproducible Python workflow. It also sets realistic expectations: an LSTM can learn patterns in historical observations, but it does not replace official forecasts, radar products, or nowcasting during fast-changing storms.
Define the forecasting task first
Start with a precise target and forecast horizon. “Weather prediction” is too broad for model development. Useful targets include:
- Temperature, relative humidity, wind speed, or pressure 1, 3, 6, or 24 hours ahead.
- Rainfall amount over the next hour or three hours.
- Probability of measurable rain during a match interval.
- A classification such as playable, rain risk, or likely interruption.
For stadium operations, probabilistic outputs are often more useful than a single number. A forecast such as “70% probability of at least 1 mm of rain in the next three hours” supports staffing, covers, broadcast planning, and spectator communication better than an apparently precise point estimate.
Treat the stadium as a location, not as a magical microclimate. A nearby weather station may be several kilometres away, while a roof, stands, pitch, and surrounding buildings can affect measurements. Record the station coordinates, elevation, distance from the venue, sensor type, and time zone. Use Asia/Kolkata consistently and store timestamps in UTC as well if data will be combined with international APIs.
Collect local and weather-specific data
Build a dataset with a clear provenance for every column. Potential inputs include:
- Hourly temperature, humidity, pressure, wind direction, wind speed, cloud cover, rainfall, and visibility.
- Historical observations from a nearby station or a suitably resolved gridded dataset.
- Official products from the India Meteorological Department where access and licensing permit.
- Forecast variables from a weather API, used carefully as inputs rather than treated as ground truth.
- Match start time, innings or session, and whether the stadium was in use. These help analyse operational impact but should not create leakage.
A match-day model can also include calendar features: month, day of year, hour, and monsoon-season indicators. Encode cyclical variables with sine and cosine transformations so that December and January are close in representation, while 23:00 and 00:00 are also treated as adjacent.
Do not scrape or redistribute data without checking terms of use. Keep an ingestion log containing source, request time, station ID, missing-value rules, and any revisions. This discipline matters more than adding another neural-network layer.
Prepare sequences without leaking future information
Sort observations by time, remove duplicates, standardise units, and resample to a fixed interval such as one hour. Missing values should be flagged explicitly. Short gaps may be interpolated for continuous sensor readings, but rainfall should not be casually interpolated because it is intermittent and highly local.
Use a chronological split rather than a random split:
- Training set: earliest historical period.
- Validation set: the following period for model and threshold selection.
- Test set: the latest untouched period.
Fit the scaler on the training set only. If the model uses the previous 24 hours to forecast the next hour, each sample has the shape (24, features). For a 6-hour forecast, either predict six values directly or use a carefully designed multi-step model. Direct multi-output prediction is usually easier to evaluate than repeatedly feeding the model’s own predictions back into itself.
import numpy as np
def make_sequences(values, target, lookback=24, horizon=1):
X, y = [], []
for end in range(lookback, len(values) - horizon + 1):
X.append(values[end-lookback:end])
y.append(target[end:end+horizon])
return np.asarray(X), np.asarray(y)Avoid a common error: normalising the complete dataset before splitting it. That allows information from the future test period to influence the training transformation and produces over-optimistic results.
Build a useful LSTM baseline
An LSTM is appropriate when recent observations and longer temporal patterns both matter. It should still be compared with simpler baselines: persistence, seasonal averages, linear regression, gradient-boosted trees, and a model that uses the latest official forecast. If an LSTM cannot beat persistence or an operational weather forecast, it is not ready for deployment.
A compact Keras model for continuous targets might look like this:
import tensorflow as tf
from tensorflow.keras import Sequential
from tensorflow.keras.layers import LSTM, Dense, Dropout
lookback = 24
n_features = X_train.shape[-1]
horizon = 1
model = Sequential([
LSTM(64, input_shape=(lookback, n_features)),
Dropout(0.2),
Dense(32, activation="relu"),
Dense(horizon)
])
model.compile(
optimizer=tf.keras.optimizers.Adam(learning_rate=1e-3),
loss=tf.keras.losses.Huber(),
metrics=[tf.keras.metrics.MeanAbsoluteError()]
)
callbacks = [
tf.keras.callbacks.EarlyStopping(
monitor="val_loss", patience=10, restore_best_weights=True
)
]
history = model.fit(
X_train, y_train,
validation_data=(X_valid, y_valid),
epochs=100,
batch_size=32,
callbacks=callbacks,
shuffle=False
)For rain occurrence, use a sigmoid output and binary cross-entropy. Because rainy hours may be less frequent than dry hours, report precision, recall, F1, PR-AUC, and a calibrated probability threshold instead of accuracy alone. For rainfall amount, consider a two-stage design: first predict whether rain occurs, then estimate amount conditional on rain. A single mean-squared-error model often predicts near-zero rainfall and misses useful peaks.
Evaluate for Bengaluru match-day decisions
Report performance by forecast horizon, season, time of day, and weather regime. A single average score can conceal failures during monsoon transitions or intense convective events. Recommended metrics include:
- MAE and RMSE for temperature, humidity, wind, and pressure.
- Brier score, reliability diagrams, and PR-AUC for rain probability.
- F1 or recall at an operational threshold for interruption alerts.
- Coverage and width of prediction intervals if uncertainty is produced.
- Comparison with persistence and the best available official forecast.
Use rolling-origin backtesting: train on an early period, validate on the next block, then move the cutoff forward. This better reflects how the system will operate after deployment. Include a separate test period containing recent observations as of 2026, but document that weather station changes and API revisions can affect comparisons.
Calibration deserves special attention. A model that says “80% rain” should be right roughly eight times out of ten over many comparable forecasts. You can calibrate probabilities on a validation set, but never tune the calibration layer on the final test set.
Improve the model only when the baseline justifies it
Useful improvements include lagged rainfall, rolling humidity statistics, pressure tendency, wind-vector components, and forecast-model variables. Add station data from multiple nearby points only after checking spatial alignment and missingness. For larger projects, compare LSTM results with temporal convolutional networks, GRUs, gradient-boosted trees, and attention-based time-series models.
For uncertainty, train quantile models or use ensembles with different initialisations and training windows. Keep the production pipeline simple enough to monitor. Track input freshness, missing fields, prediction drift, calibration, and alert frequency. Re-train on a schedule only after testing whether drift is real; automatic retraining can amplify a bad data feed.
If the project includes camera footage for wet-pitch or visibility assessment, separate that computer-vision component from the meteorological forecast and follow a dedicated computer vision model development workflow. For cloud deployment, package preprocessing and model versions together; the principles in this guide to deploying deep learning models on GKE are relevant when serving forecasts reliably.
Operational limits and responsible use
An LSTM trained on station history may struggle with rare, high-impact storms, abrupt wind shifts, sensor faults, and changes in the surrounding built environment. Never present it as an official warning system. Use it as one layer in a decision process alongside IMD alerts, radar or satellite products, ground observations, and human review.
A practical stadium dashboard should show the forecast horizon, last data update, confidence or probability, baseline comparison, and data-quality warnings. It should also preserve the model version and inputs behind every alert so staff can audit why a recommendation was made.
Frequently asked questions
How much data is needed? For hourly forecasting, aim for multiple years covering dry, monsoon, and transition periods. More data cannot compensate for inconsistent sensors or undocumented gaps.
Should I use 30 days as the lookback window? Test several windows. A 24- or 48-hour window may capture immediate persistence, while calendar features capture seasonality. The best value must be established through rolling validation.
Can this model predict rain during a match? It can estimate short-horizon risk if trained on suitable local observations, but nowcasting products and radar are often stronger for imminent convective rain.
Is an LSTM automatically better than a simpler model? No. Select it only if it consistently improves decision-relevant metrics against strong baselines and remains maintainable in production.
For Indian builders developing forecasting, monitoring, or climate-resilience products, AI Grants India can be a useful place to explore funding and support opportunities.