Rainfall forecasting for Eden Gardens is a useful applied machine-learning problem: the venue needs decisions at hourly and match-day timescales, while Kolkata’s weather shifts quickly during the monsoon. A Hidden Markov Model (HMM) can represent unobserved weather regimes—such as dry, unstable, rainy, and severe-rain conditions—and estimate how those regimes evolve over time.
An HMM will not replace a numerical weather prediction service or an official warning. It is best used as a lightweight probabilistic layer for local nowcasting, scheduling, drainage planning, and decision support. The same workflow can also inform AI predictive maintenance for railway infrastructure assets, where hidden operating conditions are inferred from sensor sequences.
Define the forecasting problem first
Before fitting a model, specify what “rainfall prediction” means. These are different tasks:
- Occurrence: Will measurable rain occur in the next 1, 3, or 6 hours?
- Intensity class: Will rainfall be light, moderate, heavy, or extreme?
- Accumulation: How many millimetres are expected over a fixed window?
- Operational impact: Is rain likely to delay play, affect pitch preparation, or overwhelm drainage?
For Eden Gardens, an hourly model is more useful for match operations than a daily model. Set a forecast horizon, define a rainfall threshold, and identify the action attached to each probability. For example, a 70% probability of rain in the next three hours may trigger a covers inspection, while a lower probability may only require closer monitoring.
Gather local and reliable data
Use the nearest defensible observations rather than assuming a city-wide average represents the stadium. Useful inputs include:
- Hourly rainfall in millimetres
- Temperature, relative humidity, pressure, wind speed, and wind direction
- Radar-derived precipitation or satellite indicators, where available
- Lightning observations and weather alerts
- Timestamp, season, missing-value flags, and station metadata
Potential sources include IMD observations and forecasts, Kolkata-area automatic weather stations, airport or university stations, and reputable weather APIs. Record the station’s distance and elevation from Eden Gardens. If the station is several kilometres away, treat its measurements as a proxy and quantify the error rather than presenting them as stadium observations.
Keep timestamps in Asia/Kolkata, preserve the original timezone, and resample all feeds to one interval. Do not fill a long missing rainfall sequence with zeros: that creates artificial dry spells. Short gaps can be imputed cautiously, but longer gaps should be excluded or marked with a missingness feature.
Convert observations into HMM states
An HMM has hidden states, observed emissions, transition probabilities, and initial-state probabilities. For rainfall, the hidden state represents a latent weather regime; rainfall and atmospheric variables are the evidence generated by that regime.
A practical first design uses four states:
- Dry: 0 mm rainfall and stable conditions
- Unsettled: intermittent drizzle or elevated rain probability
- Rainy: persistent light or moderate precipitation
- Severe: heavy rainfall, intense convective activity, or operational disruption
Do not assume the labels are learned in this order. HMM libraries often assign arbitrary state numbers, so inspect each state’s mean rainfall, humidity, and transition behaviour before naming it. Thresholds should reflect local operations and domain guidance. A cricket-ground decision threshold is not necessarily the same as an urban flood threshold.
For continuous meteorological variables, use a Gaussian or Gaussian-mixture HMM. For discretised rainfall categories, a categorical HMM is easier to explain. Rainfall is zero-inflated and highly skewed, so a single Gaussian distribution may perform poorly. Consider log1p(rainfall), a mixture model, or a two-part design that separately models rain occurrence and positive intensity.
Prepare training and test sequences correctly
Weather records are time-dependent. Randomly shuffling rows leaks future patterns into training and produces optimistic scores. Instead:
- Train on earlier seasons or months.
- Validate on a later contiguous period.
- Test on the most recent untouched period.
- Use rolling-origin evaluation for repeated operational checks.
Split by time and, where possible, test across monsoon seasons. A model that performs well in one wet month may fail during pre-monsoon thunderstorms or winter dry periods. Add calendar variables carefully, especially month or monsoon phase, but avoid features unavailable at prediction time.
Standardise continuous features using statistics from the training set only. Inspect class balance, missingness, sensor changes, and extreme rainfall events. Retain the original data so every prediction can be audited back to its source observation.
Train an HMM in Python
hmmlearn is a straightforward starting point for Gaussian HMMs. A minimal workflow is:
import numpy as np
from hmmlearn.hmm import GaussianHMM
features = ["rain_mm", "humidity", "pressure", "wind_speed"]
X_train = train[features].to_numpy()
X_test = test[features].to_numpy()
model = GaussianHMM(
n_components=4,
covariance_type="diag",
n_iter=300,
random_state=42
)
model.fit(X_train)
states = model.predict(X_test)
next_state_probabilities = model.predict_proba(X_test)[-1]Run several random initialisations because EM training can converge to different local optima. Compare models with two to six states using held-out likelihood and, more importantly, operational forecast metrics. Select the smallest model that captures meaningful regimes; extra states often memorise noise.
If you need a production service, package preprocessing, model parameters, state-label mapping, and feature version together. A deployment guide such as how to deploy deep learning models on GKE offers useful infrastructure patterns, even though an HMM itself is much lighter than a deep-learning model.
Produce useful rainfall forecasts
The Viterbi algorithm identifies the most likely sequence of hidden states after observations are available. For forecasting, use the current filtered state distribution and the transition matrix:
1. Estimate the probability of each current state.
2. Multiply by the transition matrix for the next time step.
3. Repeat for the desired horizon.
4. Convert state probabilities into rainfall occurrence or intensity probabilities.
Validate the conversion from states to rainfall carefully. A “rainy” state should not automatically mean rain in every hour. Estimate the empirical probability of each rainfall category within each state, then combine it with forecast state probabilities. Report uncertainty, not just a single label.
Evaluate what matters operationally
Accuracy alone is inadequate, especially when heavy rain is rare. Track:
- Precision, recall, and F1 for measurable-rain alerts
- Brier score and calibration curves for probabilities
- Confusion matrices for intensity classes
- Mean absolute error for rainfall accumulation
- False-alarm rate and missed-event rate
- Lead time before disruptive rainfall
Compare the HMM with simple baselines: persistence, seasonal climatology, and a rain/no-rain Markov chain. If the HMM cannot beat these baselines consistently, simplify it or improve the data. For a probabilistic forecast, calibration may matter more than marginal accuracy: a forecast issued at 60% should correspond to rain roughly six times in ten over similar cases.
Limitations and responsible use
An HMM captures temporal dependence but does not understand atmospheric physics. It may struggle with isolated thunderstorms, abrupt convective cells, station relocation, and climate shifts. It also inherits the biases of sparse or poorly maintained sensors. Do not use it as the sole basis for public safety, flood evacuation, or official match decisions.
Create a monitoring process that logs inputs, predictions, observed outcomes, model versions, and alert actions. Retrain or recalibrate when performance drifts. For richer experiments, pair the HMM with weather radar or sequence models, while keeping the HMM as an interpretable baseline. The discipline used in benchmarking NLP models for Telugu and Sanskrit—fixed datasets, explicit metrics, and reproducible comparisons—applies equally well here.
Practical deployment checklist
- Define the forecast horizon and rainfall threshold.
- Confirm station location, timezone, units, and data licence.
- Build a time-based evaluation split.
- Compare categorical, Gaussian, and mixture-emission approaches.
- Test multiple state counts and random seeds.
- Calibrate probabilities on a validation period.
- Set alert thresholds with venue operators.
- Monitor drift during and between monsoon seasons.
- Preserve an audit trail for every forecast.
A well-designed HMM can provide Eden Gardens with transparent, low-cost rainfall probabilities and regime summaries. Its value comes from disciplined data engineering, honest uncertainty, and comparison with stronger weather baselines—not from treating a compact statistical model as a complete forecasting system.
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