The rainfall prediction using machine learning plays a vital role in various fields in agriculture, water resource management and urban planning. To predict rainfall, machine learning models use the conditions of atmosphere and the historical rainfall data and other characteristics.
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The step-by-step procedure to set up our rainfall prediction project as follows,
- Define the Objective:
First, it should be decided that whether we want to predict binary outcomes or continuous outcomes. The binary outcomes provide the data about rain or no rain whereas continuous outcomes depict the amount of rainfall. It determines the prediction horizon like, next day, next week, next month, etc.
- Data Collection:
- Weather Datasets: We can gather the historical weather data which generally includes, atmospheric pressure, humidity, temperature, wind speed and wind direction, etc. This sources which also includes local meteorological departments or global datasets like NOAA’s (National Oceanic Atmospheric Administration) datasets.
- Remote Sensing Data: The cloud cover information provided by the satellite images which is very useful to us for data collection.
- Data Pre-processing:
- Handling Missing Data: The values are missed sometimes in weather datasets, so we should decide whether to remove or impute them.
- Temporal Features: It brings out the features like day of the week, month and season which might results in great impact on rainfall patterns.
- Lagged Features: we are able to determine the rainfall through the conditions of previous days. It involves characteristics from the earlier time steps such as temperature from the previous day.
- Feature Engineering:
- Domain Knowledge: It mainly depends on atmospheric conditions like, when high humidity is integrated with definite wind patterns might indicates rainfall.
- Correlation Analysis: We analyse the features which is most related with rainfall to understand their importance.
- Model Selection and Training:
- For Binary Prediction (rain/no rain): Some tools are used by us to predict binary predictions are Decision trees, Logistic Regression, Random Forests and Support Vector Machine (SVM) etc.
- For Continuous Prediction (amount of rainfall): To detect the continuous prediction, we utilize tools like Linear Regression, Decision trees and Neural Networks.
The Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM) are the time series forecasting methods is very beneficial for providing us the sequential nature of weather data. It uses the historical data for training and validation. It requires the current data for testing to simulate the prediction of real world.
- Evaluation:
- For Binary Prediction (rain/no rain): Accuracy, Precision, Recall, F1 Score are very essential for this prediction.
- For Continuous Prediction (amount of rainfall): Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), R2 Score are the tools used by us to detect continuous prediction.
It evaluates the variations between actual visualization and predicted values to scale the performance of model visually.
- Deployment:
If our objective is to provide daily weather forecasts means then consider real-time prediction systems. Creating a web-based dashboard or mobile application helps us to circulate rainfall predictions to public.
- Continuous Learning:
When the present weather data becomes available to us, and then constantly retrain the model with enhancements. This makes sure that the model is ready to adjust new patterns or climate change.
Challenges:
- Complex Interactions: The weather is determined by multiple interacting factors which make prediction more complex to us.
- Spatial Variability: Rainfall is appeared locally. The trained model data from one region may not possibly perform well on another region.
- Temporal Variability: Due to climate change or some other factors, the historical patterns of rainfall is modified.
Extensions:
- Multi-Modal Data Integration: We hybrid the weather station data with remote sensing data for more extensive features.
- Weather Radar Data: To advance short-term rainfall predictions, then we add the real-time radar data.
The collaboration and interaction with meteorologists or climatologists can provide the beneficial information with their domain knowledge which makes our rainfall predicting project as a more robust and error-free model.
Rainfall Prediction Using Machine Learning Research Thesis Topics
The very most interesting topics are described be in touch with us to explore more in this field.
- Rainfall Prediction Using Machine Learning
Keywords
Forecasting rainfall, Machine learning algorithms, Random Forest
Various ML methods are utilized in this research to predict the rainfall. For that, we examined and compared different ML approaches like Random Forest, Extra Trees, Adaptive Boosting, Gradient Boosting, Multilayer Perceptron, and Gaussian naïve Bayes. As a result, the Random Forest and Extra Tree classifiers achieved better results when compared to other ML techniques.
- Performance Evaluation of Machine Learning Algorithms for Rainfall Prediction Using Dimensionality Reduction Techniques
Keywords
Climate change, Pattern recognition, Predictive models, neural networks
To examine the performance of various ML techniques for predicting rainfall patterns by utilizing dimensionality reduction methods on climate change indicators is the ultimate goal of this study. For feature selection, techniques such as Principle component analysis (PCA), Pearson correlation, and Greedy search algorithms are utilized. Finally, Bayesian linear regression method provides effective outcome followed by neural network regression.
- Application of Innovative Machine Learning Techniques for Long-Term Rainfall Prediction
Keywords
Rainfall prediction, multiple linear regression, Support vector regression, Multivariate adaptive regression splines
Implementation of various ML methods like, multiple linear regression (MLR), support vector regression (SVR), multivariate adaptive regression splines (MARS), and random forest (RF), for daily and weekly rainfall prediction is the main focus of the paper. As a result, RF outperformed other ML approaches and provides a possibility for daily and weekly rainfall prediction.
- Rainfall Prediction Rate in Saudi Arabia Using Improved Machine Learning Techniques
Keywords
Agricultural, rainfall patterns, novel classification technique, Saudi Arabia
This paper goal is to evaluate the efficiency of multiple ML techniques in predicting rainfall. The DM methods are also utilized in this paper that outperformed traditional statistical methods. The ML based classification method is employed to evaluate whether the forecasted rainfall would be heavy or normal. Based on this, DT showed greatest efficiency and FFANN classifier provides better results in predicting rainfall than other ML methods.
- Application of machine learning ensemble models for rainfall prediction
Keywords
Dagging, Ensemble models, Performance evaluation, SMO
For rainfall prediction, we suggested a novel standalone SMO regression model and integrate it with Dagging (DA), random committee (RC), and additive regression. Then, based on the Pearson correlation coefficient among input and output variables, various input scenarios were constructed. As a result, DA-SMO ensemble model achieved efficient end results than other algorithms.
- Rainfall Prediction System Using Machine Learning Fusion for Smart Cities
Keywords
Data fusion, fuzzy system, smart cities, big data, hydrological model, information systems, precipitation
A new actual time rainfall forecasting model for smart cities was suggested in this study by utilizing ML fusion methods. Various supervised ML methods such as decision tree, Naive Bayes, K-nearest neighbors, and SVM were used in this study. The fuzzy logic was employed in the suggested framework for efficient rainfall prediction. Before the classification procedure, preprocessing steps like cleaning and normalization were performed.
- Rainfall prediction: A comparative analysis of modern machine learning algorithms for time-series forecasting
Keywords
LSTM Networks, Multivariate time-series, multi-step forecast, Time-series data
A comparative study utilizing rainfall prediction model based on traditional ML methods and Deep Learning approaches were proposed. For predicting rainfall using time-series data , LSTM, Stacked-LSTM, Bidirectional-LSTM, XGBoost, and an integration of Gradient Boosting Regressor, Linear Support Vector Regression, and an Extra-trees Regressor based frameworks were compared. As a result, LSTM-Networks with fewer hidden layers outperform other models.
- Machine learning models for prediction of rainfall over Nigeria
Keywords
Statistical model, Tropical rainfall, Rainfall modeling
To predict monthly and annual rainfall, two multivariate polynomial regressions and various ML methods such as artificial neural networks (ANN), adaptive neuro-fuzzy inference system (ANFIS) and SVM algorithms were used. Results showed that ANFIS achieved better performance than other methods and various ANFIS algorithms like ANFIS-GBELL, ANFIS-GAUSS, ANFIS-SC, and ANFIS-FCM were used for particular months to predict rainfall.
- Prediction of Rainfall in Australia Using Machine Learning
Keywords
Meteorological phenomena, knn, decision tree
This research demonstrated about the utilization of several machine learning methods to predict rainfall. Different machine learning approaches such as knn, decision tree, random forest, and neural networks were used in this study. As a result, neural networks achieved best results than other methods in predicting rainfall.
10. An Extensive Review of Rainfall Prediction using Machine Learning and Deep Learning Techniques
Keywords
Deep learning, Predictive models, Prediction algorithms, Reservoirs
The utilization of Machine learning and Deep learning approaches are examined in this research to predict the rainfall. Deep learning prediction methods such as convolution neural network, long short-term memory and Machine learning prediction methods such as regression were examined in this study. This research also described about the advantages and disadvantages of utilizing neural network techniques and machine learning techniques.
