One of the most important factor is using the relevant features, by selecting the features that have the maximum correlation with the predicted value, Note here that the correlation is not necessarily to be found mathematically or by using the standard machine learning data preprocessing, the feature selection can be done manually by the intuitive sense to decide if the feature is relevant or not.

If you have selected only the relevant features, using simple machine learning model, for example a shallow neural network, can be excellant and even better than any more complex model, I recommend using the simplest technique and gradually increase the complexity if needed. Deep learning models are used when it is very hard to find the relevant features manually, like in images datasets, ensemble models can be effecient for mitigiating some problems like overfitting, little data and some others, if you have enough data, You may not need ensemble learning. Another case when you may need a special type of ensemble learning that sometimes called "Multi-Modal ML Model" in which you use different data modalities, for example, you have numerical data about the temperature of the last few months, you also have data with different form that catches different aspect, like video, image, voice, ....etc here, the model of processing the numerical data is not the same as the one which processes video or voice data, here you may build correspondant model for each data modality, then aggregate the models to get the advantages of each one.

About spatial and Temporal data, I don't know exactly what are the spatial and temporal information in rainfall prediction, but generally, you mean here Time Series analysis where the amblitude of time series is the spatial information and the timestamp, or interval or frequency is the temporal data, however, you can not process one of them independently from the other. For this reason, you may use different techniques, RNN models like LSTM is good, but it is still hard to interpret the results sometimes.

I recommend a technique called time series imaging in which you convert each window of the time series to an image using some techniques like RP, GAF, and others. Those images capture both sapatial and temoralinformation as images, you may use then CNN models or simple NN models to predict the corresponding output.

Rainfall prediction, or weather data in general, lends itself as a suitable candidate for latest AI techniques including Deep learning. I am saying this due to the fact that weather monitoring has become a common feature of internationally maintained datasets. Machine learning techniques rely on accurate and large amounts of data for reliable predictions which is highly plausible with the current trends of weather monitoring.

Moreover, machine learning techniques provide the flexibility to work with data without prior knowledge which is extremely critical with the changing global climatic trends. Last but not the least, these techniques provide a robust interface with the real-time which is one of the prominent reasons of their usage in modern applications of weather systems including websites and applications.

Models like LSTM and GRU work beautifully in handling the time series of rainfall, while CNNs are suited for spatial data, i.e., images from satellites. Ensemble methods like XGBoost or random forests combine a bunch of models to get increased accuracy and robustness.

Some issues to bear in mind are numerous. Rainfall data is highly variable both over space and time and hence is difficult to forecast. To solve this, we can use hybrid models made up of more than one type of algorithm to understand spatial as well as temporal relationships. Feature selection has a big job too—picking the correct weather parameters will ruin or improve the model. Preprocessing must be done in the right manner for missing values, outliers, and noise, to achieve a good model.

Dear Himanshu Tiwari

Accurate rainfall prediction is complex due to its highly non-linear, spatio-temporal nature. However, machine learning (ML) and deep learning (DL) models have shown great promise in improving the precision of such forecasts by learning patterns from large historical datasets.

1. Data is the Foundation

The success of any model depends on the quality of input data. Key features often include:

• Meteorological variables: temperature, humidity, wind speed, pressure, etc.
• Satellite or radar images (for spatial context)
• Geographical attributes (latitude, elevation, etc.)

Cleaning the data, handling missing values, and selecting only the most relevant features are crucial preprocessing steps.

2. Machine Learning Models

Traditional ML models like Random Forest, XGBoost, and Support Vector Machines (SVM)perform well using structured tabular data. These models:

• Are easy to interpret
• Work well on small to medium datasets
• Handle non-linear relationships effectively

They are good starting points when considering importance and explainability.

3. Deep Learning Techniques

When larger datasets or complex patterns are involved, DL becomes powerful:

• LSTM/GRU models capture time-dependent rainfall patterns from past weather data.
• CNNs work well with satellite images to detect spatial patterns like cloud movement.
• Hybrid CNN-LSTM models or ConvLSTM integrate spatial and temporal learning, making them highly suitable for spatio-temporal rainfall prediction.

4. Ensemble and Hybrid Methods

To enhance robustness and accuracy:

• Ensemble techniques like Random Forest or XGBoost combine predictions from multiple models to reduce error.
• Hybrid models can combine multiple data types (e.g., satellite images and weather readings) by assigning appropriate models to each modality and fusing their outputs.

These are especially useful when data is diverse or collected from multiple sources.

5. Best Practices

• Use domain knowledge to guide feature selection.
• Start with simple models and scale up complexity as needed.
• If using time-series data, maintain the sequence during training.
• Always validate your model using appropriate metrics like RMSE, MAE, or F1-score, depending on whether the output is continuous or categorical.

Conclusion

Rainfall prediction benefits greatly from machine learning when models are thoughtfully matched to the data type and forecasting goals. We can build robust, accurate, and scalable forecasting systems by combining structured data, satellite imagery, and hybrid architectures.