Precise Weather Prediction with Optimization of Machine Learning Algorithms and Hybrid Feature Selection Techniques

Aug 3, 2026

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Weather prediction is vital for agriculture, disaster management, transportation, and other risk-sensitive planning activities. The study implements and evaluates multiple machine learning models—decision trees, random forests, and support vector machines—using the WEKA tool. To improve both accuracy and efficiency, a hybrid optimized feature selection strategy (PSO + RF) is applied to strengthen predictive performance. Results show the optimized hybrid feature selection with Random Forest classification achieves about 97% accuracy and reduces prediction errors, supporting more dependable weather forecasting and better decision-making under weather-dependent scenarios.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages13

Reading Time33 min

AccessFree to download

Introduction

Methodology

Data Collection and Preprocessing

Feature Selection

Precise Weather Prediction with Optimization of Machine Learning Algorithms and Hybrid Feature Selection Techniques 1Navita, 2Dr. S. Srinivasan, 3Dr. Nitin, 1Research scholar in computer science and applications PDM University bahadurgarh, jhajjar, India 2Professor, Department of Computer science and applications, PDM University, Bahadurgarh, Jhajjar, India 3Professor, Department, Computer science and applications, PDM University, Bahadurgarh, Objective: Apply Machine Learning Algorithms with Optimized Feature Selection Techniques Using WEKA Tool. Abstract: Weather prediction is crucial for various sectors including agriculture, disaster management, and transportation, as it helps in mitigating risks and planning effectively. This study focuses on the implementation and evaluation of several machine learning algorithms, specifically decision trees, random forests, and support vector machines, using the WEKA tool. These algorithms are employed to predict weather-related outcomes. To enhance the performance of these models, an optimized hybrid feature selection technique (PSO + RF) is applied, aiming to improve both accuracy and efficiency. The optimized hybrid feature selection combined with Random Forest classification significantly outperforms other techniques, achieving an impressive accuracy of 97% . The results demonstrate that incorporating optimized feature selection significantly enhances the predictive capabilities of the machine learning models, providing a robust approach for accurate weather forecasting. This study underscores the potential of advanced machine learning techniques in improving weather prediction, thereby contributing to better decision-making and risk management in weather-dependent activities. Keywords: Optimization,Algorithms, Hybrid Feature 1. Introduction Predicting the weather has been an important subject of research for a long time, and it has an impact on many other fields, including agriculture, aviation, disaster management, and activities that people do on a daily basis. For the sake of planning and minimizing the negative impacts that are brought about by unforeseen weather conditions, accurate weather forecasting is necessary. The numerical weather prediction (NWP) models that make use of physical equations to mimic the behavior of the atmosphere have been the primary method that meteorologists have depended on all throughout history. On the other hand, these models frequently have difficulty dealing with the intricate patterns and non-linear interactions that are present in the meteorological data, which results in limitations in the accuracy of their predictions. It has been more apparent in recent years that machine learning (ML) is a potent instrument for weather forecasting. Machine learning models have the ability to make use of previous data in order to learn and recognize patterns that are difficult to capture using conventional approaches. Although these models have demonstrated that they have the potential to improve the accuracy of weather forecasts, they also confront a number of problems. The process of selecting useful characteristics from enormous volumes of meteorological data is a significant problem that might have an impact on the performance of the model. The approaches of feature selection are extremely important for determining which variables are the most useful, lowering the dimensionality of the model, and improving its efficiency. In order to meet the difficulty of accurate weather forecasting, the purpose of this study is to integrate the optimization of machine learning algorithms with hybrid feature selection approaches. For the purpose of making weather forecasting models more dependable for use in practical applications, the goal is to improve the accuracy and efficiency of these models. In order to obtain higher prediction performance, the research suggests a unique methodology that combines a number of different machine learning al

FAQs

Why is accurate weather prediction important?

Accurate weather forecasting supports planning and reduces negative impacts from unexpected weather conditions. It is especially relevant to agriculture, aviation, disaster management, and daily activities.

Which machine learning algorithms are evaluated in this study?

The study evaluates decision trees, random forests, and support vector machines using the WEKA tool for weather outcome prediction.

How does the optimized hybrid feature selection improve performance?

A hybrid feature selection technique combining PSO and RF selects more informative variables. This reduces input dimensionality while improving predictive accuracy and efficiency, with Random Forest achieving about 97% accuracy.

Aug 3, 2026

Upload by Rowan