A novel “Hybrid Parallel Architecture Integrating FFN, 1D CNN, and LSTM” is presented to enhance wildfire prediction capabilities in Morocco. Utilizing the “Morocco Wildfire Predictions: 2010–2022 ML Dataset”, this model merges various data points, including meteorological conditions, human population density, and environmental factors like soil moisture and vegetation indices. The architecture combines feedforward neural networks (FFN), one-dimensional convolutional neural networks (1D CNN), and long short-term memory networks (LSTM) to process different subsets of features in parallel, capturing both spatial and temporal dependencies crucial for accurate wildfire predictions.
The dataset covers data from 2010 to 2022 and includes standardized features specifically prepared for machine learning applications in disaster management. By training this hybrid model, significant validation accuracy was observed, indicating robust performance. Initial results were promising, with the model achieving an accuracy of 83.96% on the training set and 87.19% on the validation set during the first epoch, improving to 87.13% on the training set and 87.56% on the validation set by the second epoch.
These results underscore the efficacy of integrating multiple neural network architectures to enhance predictive accuracy in critical applications such as wildfire prediction. The findings suggest substantial potential for deploying such models in real-world scenarios, aiding policymakers and disaster management teams in deploying timely and effective responses to mitigate wildfire impacts.
