Weather Prediction using Advanced Machine Learning Techniques

Abstract

Prediction of weather condition is important to take efficient decisions. In general, the relationship between the input weather parameters and the output weather condition is non linear and predicting the weather conditions in non linear relationship posses challenging task. The traditional methods of weather prediction sometimes deviate in predicting the weather conditions due to non linear relationship between the input features and output condition. Motivated with this factor, we propose a neural networks based model for weather prediction. The superiority of the proposed model is tested with the weather data collected from Indian metrological Department (IMD). The performance of model is tested with various metrics..

Analisis Model Prediksi Cuaca Menggunakan Support Vector Machine, Gradient Boosting, Random Forest, Dan Decision Tree

“…Hemalatha, dkk. (2021) mengembangkan prediksi cuaca menggunakan Teknik machine learning untuk membuktikan bahwa parameter input cuaca dan output kondisi cuaca memiliki hubungan tidak linier [9]. Fowdur, dkk.…”

Deep learning model for temperature prediction: A case study in New Delhi

“…In recent years, researchers have shown great interest in the usage of ML models for weather prediction (Bochenek & Ustrnul, 2022; Hemalatha et al, 2021). A significant research contribution is made by some researchers using ML and DNNs.…”

“…Hemalatha et al (2021) came up with a model for predicting the weather based on neural networks. Weather data from the Indian Metrological Department is used to test how well the proposed model works (IMD).…”

“…This research found that the ANN with MLP model proved more accurate in forecasting relative humidity than the SARIMA model. Hemalatha et al (2021) came up with a model for predicting the weather based on neural networks. Weather data from the Indian Metrological Department is used to test how well the proposed model works (IMD).…”

“…On the other hand, machine learning (ML)‐based models are fast, reliable, and cheaper methods to forecast weather (Biau, 2012; Biswas et al, 2014; Dolara et al, 2017; Gadekallu et al, 2019; Raed et al, 2010; Sahai et al, 2000; Shukla & Mooley, 1987; Voyant et al, 2012). Accurate prediction of weather is a tough job because of a nonlinear relationship between input features and the output atmospheric conditions (Hemalatha et al, 2021; Yalavarthi & Shashi, 2009). Supervised ML techniques are popular approaches for prediction or estimation problems.…”