Marathwada’s weather is difficult to forecast at useful local scales. Rainfall is highly uneven, dry spells can interrupt the southwest monsoon, and extreme heat or intense short-duration rain can affect crops, water planning, and rural infrastructure. A model that performs well on a national benchmark may still fail for a farmer, irrigation planner, or district administrator in Beed, Latur, Nanded, Dharashiv, Chhatrapati Sambhajinagar, Hingoli, Jalna, or Parbhani.
Long Short-Term Memory (LSTM) networks can learn patterns across ordered observations such as daily temperature, humidity, wind, pressure, and rainfall. They are useful when the forecast depends on several preceding days, but they are not automatically better than simpler statistical or tree-based models. The strongest project combines an LSTM with disciplined data engineering, location-specific validation, and a clear operational use case.
Define the forecast before choosing the model
Start with a precise target. “Predict the weather” is too broad for a reliable system. Choose one or more of the following:
- Next-day minimum and maximum temperature
- Rainfall total over the next 24 hours
- Probability of measurable rain, such as at least 2.5 mm
- Rainfall accumulation over the next three or seven days
- Heatwave or heavy-rain alert probability
Also define the forecast location and horizon. A district-level seven-day rainfall outlook requires different data and evaluation from a station-level next-day temperature forecast. For agriculture, a calibrated probability of rain may be more useful than a single millimetre estimate because it supports decisions such as spraying, sowing, irrigation, and harvesting.
If the project will eventually serve multiple districts, design it as a reusable pipeline rather than a notebook for one station. Guidance on implementing scalable ML pipelines for predictive analytics is relevant here, particularly for scheduled ingestion, versioned features, monitoring, and retraining.
Collect and audit Marathwada weather data
Use the most local, consistent observations available. Potential sources include:
- India Meteorological Department observations and gridded products
- Automatic weather stations operated by government departments, universities, or agricultural institutions
- Reanalysis data for gap filling and broader atmospheric variables
- Satellite-derived rainfall and land-surface indicators
- Soil moisture, elevation, vegetation, and crop-calendar data where the use case justifies them
Before modelling, create a data dictionary recording units, timestamps, station coordinates, sensor changes, and missing-value codes. Convert all timestamps to Indian Standard Time where appropriate, and make sure “daily rainfall” uses the same accumulation window across sources. A midnight-to-midnight value is not interchangeable with a meteorological 24-hour accumulation.
Run quality checks for impossible values, duplicated timestamps, sudden sensor shifts, long gaps, and suspiciously repeated measurements. Do not blindly replace missing rainfall with a mean: that can erase exactly the extreme events the model must learn. Short gaps may be interpolated for temperature, while rainfall gaps should often be flagged, imputed using a documented method, or excluded from particular training windows.
Engineer features that reflect local weather
An LSTM can learn temporal structure, but feature design still matters. Useful inputs may include:
- Recent daily values and rolling totals for rainfall, temperature, humidity, pressure, and wind
- Seven-, 14-, and 30-day rainfall accumulation
- Number of consecutive dry days and wet days
- Day of year encoded as sine and cosine to represent seasonality
- Monsoon-phase indicators, crop calendars, elevation, and station coordinates
- Satellite or reanalysis variables that capture regional weather systems
Rainfall is zero-heavy and highly skewed. For a direct regression model, consider transforming the target with log1p, but evaluate results after reversing the transformation. For rain occurrence, train a classification model; for rainfall amount, use a separate conditional regression model. A two-stage “rain/no rain plus amount” approach is often more practical than forcing one model to handle both outcomes.
Fit scalers only on the training period. Applying a scaler to the full dataset before splitting introduces future information and produces optimistic results. Save the fitted scaler with the model so deployment applies precisely the same transformation.
Build a leakage-resistant LSTM pipeline
Convert the time series into supervised sequences. For example, a 30-day input window can predict rainfall tomorrow or the next seven-day total. Each training example should contain only information that would have been available at prediction time.
A sensible baseline is a single LSTM layer followed by dropout and a dense output layer. Keep the first model small; larger networks can memorise station-specific noise, especially when observations are limited. Use Adam, a monitored validation loss, early stopping, and a learning-rate schedule. Candidate settings should be selected with time-aware validation rather than random cross-validation.
Compare the LSTM against strong baselines:
- Persistence: tomorrow resembles today
- Seasonal average for the same period of the year
- Moving-average or exponential-smoothing forecast
- Linear regression or a tree-based model using lagged features
If the LSTM cannot beat these baselines consistently, it is not ready for deployment. Teams new to architecture choices can review how to create custom neural networks in Python, but weather forecasting demands more than selecting layers and changing epochs.
Validate by time, season, and location
Never use a random 80:20 split for a temporal forecast. Train on earlier dates, validate on a later block, and reserve the most recent period as a final test set. Include at least one monsoon season in the holdout, and preferably test separately on monsoon, winter, and pre-monsoon periods.
For a multi-station model, use a spatial holdout as well: leave one or more stations or districts out during testing. This reveals whether the model generalises beyond the locations it has memorised. Track MAE and RMSE for temperature, but add classification metrics for rain occurrence, including precision, recall, F1, ROC-AUC, and especially precision-recall performance when rain events are rare. For rainfall quantities, report bias and errors on rainy days separately from all days.
Plot predicted versus observed values, residuals by month, missed heavy-rain events, and performance by station. A model with a good average score but systematic underprediction of intense rainfall may be unsafe for alerts. Use prediction intervals or calibrated probabilities where decisions carry financial or safety consequences.
Deploy forecasts responsibly
A useful system needs more than an API that returns a number. Schedule data ingestion, validate each new batch, generate forecasts, store model and feature versions, and expose the forecast with its issue time, location, horizon, and uncertainty. Display “data unavailable” rather than silently using stale observations.
Monitor drift in sensor distributions, missingness, forecast errors, and seasonal performance. Retrain on a documented schedule, but do not overwrite the production model without a backtest and approval process. For applications involving crop insurance or yield decisions, combine weather forecasts with satellite-based yield prediction for insurance providers in India only after checking alignment of spatial resolution, dates, and uncertainty.
Communicate forecasts in decision-ready language: probability of rain, expected range, confidence, and recommended verification through official warnings. An LSTM should support—not replace—IMD alerts, local observation, and professional meteorological judgement.
A practical implementation checklist
- Define one target, horizon, geography, and user decision.
- Assemble station, gridded, satellite, and contextual data with documented provenance.
- Audit timestamps, units, gaps, outliers, and sensor changes.
- Create lag, rolling, seasonal, dry-spell, and spatial features.
- Split chronologically and reserve a genuinely unseen test period.
- Compare against persistence, seasonal, statistical, and tree-based baselines.
- Train a compact LSTM with early stopping and leakage controls.
- Evaluate extreme events, rainy days, seasons, and locations separately.
- Deploy with uncertainty, monitoring, versioning, and a fallback forecast.
The goal is not to claim that an LSTM can predict every local shower. The goal is a tested, transparent forecast service that improves a specific decision in Marathwada and makes its limitations visible.