Authors : Vishaal S

Volume/Issue : Volume 10 - 2025, Issue 5 - May

Google Scholar : https://tinyurl.com/bdfbc5h9

DOI : https://doi.org/10.38124/ijisrt/25may1194

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Weather forecasting is an important task in disaster management, especially for a city like Chennai, which is commonly affected by cyclones, heavy rainfall, and heat waves. The proposed study investigates the enhancement of the accuracy and reliability of weather prediction models with the help of the introduction of FeedForward Neural Networks (FFNNs) with the use of Rectified Linear Unit (ReLU) activation functions. Statement: Traditional activation functions like the sigmoid function are not effective activating functions as they suffer from vanishing gradient problems that do not allow deep networks to perform. In order to avoid these complications, FFNNs with ReLU, which allows for high efficiency and sparsity, are used to process large-scale meteorological datasets. The model is trained on historical weather data that is collected and preprocessed, and its performance is evaluated using metrics such as Mean Squared Error (MSE) and Root Mean Squared Error (RMSE). The findings reveal that the proposed approach leads to a notable enhancement in short- term prediction skill, particularly regarding the key physical parameters of temperature and wind speed. The early stopping and dropout layers actually reduce overfitting. These results demonstrate the potential of FFNNs for transforming weather forecasting systems to provide actionable information on extreme weather events risk for disaster management and decision- making in regions exposed to extreme weather events. Our research builds on an expanding body of literature around optimizing neural networks for meteorological applications and suggests areas for further research that could improve robustness and scalability. Citation: Palak Bansal Institute of Higher Education Research, Mandi 174323, India Abstract Accurately predicting the weather remains challenging, leading to injuries and deaths across the globe due to natural disasters.

Keywords : Weather Forecasting, Disaster Management, FeedForward Neural Networks (FFNNs), Rectified Linear Unit (ReLU), Meteorological Datasets.

References :

1. Prediction of weather forecasting using artificial neural networks A..Ajinaa* Jaya Christiyan K. G.b Dheerej N Bhatc Kanishk Saxenac.
2. Forecasting Seasonal Rainfall using a Feed Forward Neural Network with Back-Propagation: A Case of Zambia Lillian Mzyece1 University of Zambia School of Natural Sciences Department Computer Science Lusaka. Zambia Email: [email protected] Mayumbo Nyirenda PhD2 University of Zambia School of Natural Sciences Department Computer Science Lusaka. Zambia Email: [email protected] Prof Jackson Phiri PhD3 University of Zambia School of Natural Sciences Department Computer Science Lusaka. Zambia Email: [email protected].
3. A Study of Precipitation Forecasting for the Pre-Summer Rainy Season in South China Based on a Back-Propagation Neural Network Bing-Zeng Wang 1,2, Si-Jie Liu 1,2,*, Xin-Min Zeng 1,2,3,* , Bo Lu 4, Zeng-Xin Zhang 1, Jian Zhu 1 and Irfan Ullah.
4. A Study of Precipitation Forecasting for the Pre-Summer Rainy Season in South China Based on a Back-Propagation Neural Networ
5. An Efficient Weather Forecasting System using Artificial Neural Network Dr. S. Santhosh Baboo and I. Kadar Shereef.
6. A Novel Ensemble Wind Speed Forecasting System Based on Artificial Neural Network for Intelligent Energy Management MERVE ERKINAY OZDEMIR Department of Electrical and Electronic Engineering, Iskenderun Technical University, 31200 Iskenderun, Turkey.
7. Improving Data‐Driven Global Weather Prediction Using Deep Convolutional Neural Networks Authors: Wenchang Yang, Andrew R. Solow, and Peter H. Stone.
8. Leveraging Machine Learning Algorithms for Improved Disaster Management Authors: Linardos et al.
9. An Efficient Deep Learning Model for Global Weather Forecast Authors: Chen et al.
10. An Integrated Approach for Weather Forecasting and Disaster Prediction Using Deep Learning Architecture Based on Memory Augmented Neural Networks Authors: Satwik and Sundram