A Comprehensive Analysis of Weather Prediction Model Using Machine Learning Techniques

Forecasting rainfall is critical for water-resource management, agriculture and disaster mitigation. Classical models that use statistical methods have been used for decades to predict rainfall, but their ability is limited because they do not properly model the intricacies of meteorological data. The limitation of these models is primarily they are poor at handling big data and non-linear relationship among the <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</sup>tmospheri<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</sup> variables, which decreases their accuracy in predicting rainfall. To make famous the limitations characteristic to those problems, this paper investigates how Machine Learning techniques can be used more properly and predicts rainfall in greater form by using time-series having an intricate structure based on utilizing numerous algorithms which also employ unique advanced learning mechanisms. In our work, we build machine learning models relying on meteorological data from Austin TX located in humid subtropical climate. Key atmospheric predictors that affect precipitation, such as temperature, humidity etc., are determined. This dataset which is obtained from Kaggle, will be utilized to train various machine learning models such as Multivariate Linear Regression, Random Forest Regressor, Support Vector Regression and Multilayer Perceptron Regressor. Performance Metrics like Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) are used to evaluate the performance of each model. In this study model with the optimum accuracy will be useful to predict the rainfall and also by comparing-and-analysis of models, we can understand how other models are such efficient in predicting anything. Based on the initial results, it appears that Random Forest Regressor and Support Vector Regression produce lower errors with greater predicti<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ve</sup> accuracy than other algorithms. The importance of feature engineering and data preprocessing for the final model performance are also touched on. The following some challenges like data imbalance and overfitting, the study shows that machine learning based methods provide a scalable, cost-effective methodologies with as an equally accurate model compared to existing rainfall prediction techniques. The system also has potential to greatly improve accuracy of weather forecasting, with implications for agriculture and urban planning as well disaster response. The next generation of efforts will refine these models for real-time prediction and generalizing to other regions areas.