Wildfire path spread prediction system using machine learning: use of ANN, convolutional autoencoder, and ConvLSTM ML model
| dc.contributor.advisor | Winberg, Simon | |
| dc.contributor.author | Makhaba, Limpho Mapulane | |
| dc.date.accessioned | 2025-12-02T13:09:18Z | |
| dc.date.available | 2025-12-02T13:09:18Z | |
| dc.date.issued | 2025 | |
| dc.date.updated | 2025-12-02T13:00:05Z | |
| dc.description.abstract | Over the years, the frequency and intensity of wildfires have increased due to climate change, old fire management practices, and other environmental factors [1]. These wildfires pose a threat to ecosystems, infrastructure, property, and lives, particularly in highly susceptible Mediterranean regions, with suitable climatic conditions characterized by hot, dry summers and cool, humid winters [2]. Although fire is essential to maintaining such ecosystems' health, it can destroy long-term environmental sustainability and cause devastating destruction [1, 3]. Wildfire spread prediction from the ignition point to the surrounding area is a complex phenomenon affected by multiple variables such as weather, topography, and land-cover characteristics. Traditional systems used in wildfire spread prediction are often limited in accounting for the dynamic and complex factors influencing fire behaviour. To address this challenge, this research uses a multidisciplinary approach that combines Machine Learning techniques, remote sensing data, and wildfire science to develop an advanced wildfire prediction system. This research performs a comprehensive investigation into the use of supervised classification Machine Learning(ML) models, specifically, Artificial Neural Networks(ANN), Convolutional Autoencoder, and Convolution Long Short-Term Memory(convLSTM), in predicting wildfire spread. The three ML models developed were based on domain knowledge of fire behaviour and utilised Google Earth Engine(GEE) weather data and Sentinel Hub burned scars satellite data to train a logistic method for simulating fire burn maps. The ML models were then trained, tested, and validated using this data. An analysis of the models' capabilities in predicting the propagation of wildfire spread, as well as the driving factors that affect wildfire behaviour, was performed. The following evaluation matrices: loss, precision, recall, Area Under the Curve-Precision Recall(AUC_PR) and F1 score are used to assess the performance of the models in predicting the spatial and temporal properties of wildfire spread. The results indicate that the convLSTM can predict both temporal and spatial properties of fire, while the Convolutional Autoencoder can predict only the spatial properties of fire with minimal loss. The ANN produced the least satisfying results in predicting fire's spatial properties. | |
| dc.identifier.apacitation | Makhaba, L. M. (2025). <i>Wildfire path spread prediction system using machine learning: use of ANN, convolutional autoencoder, and ConvLSTM ML model</i>. (). University of Cape Town ,Faculty of Engineering and the Built Environment ,Department of Electrical Engineering. Retrieved from http://hdl.handle.net/11427/42379 | en_ZA |
| dc.identifier.chicagocitation | Makhaba, Limpho Mapulane. <i>"Wildfire path spread prediction system using machine learning: use of ANN, convolutional autoencoder, and ConvLSTM ML model."</i> ., University of Cape Town ,Faculty of Engineering and the Built Environment ,Department of Electrical Engineering, 2025. http://hdl.handle.net/11427/42379 | en_ZA |
| dc.identifier.citation | Makhaba, L.M. 2025. Wildfire path spread prediction system using machine learning: use of ANN, convolutional autoencoder, and ConvLSTM ML model. . University of Cape Town ,Faculty of Engineering and the Built Environment ,Department of Electrical Engineering. http://hdl.handle.net/11427/42379 | en_ZA |
| dc.identifier.ris | TY - Thesis / Dissertation AU - Makhaba, Limpho Mapulane AB - Over the years, the frequency and intensity of wildfires have increased due to climate change, old fire management practices, and other environmental factors [1]. These wildfires pose a threat to ecosystems, infrastructure, property, and lives, particularly in highly susceptible Mediterranean regions, with suitable climatic conditions characterized by hot, dry summers and cool, humid winters [2]. Although fire is essential to maintaining such ecosystems' health, it can destroy long-term environmental sustainability and cause devastating destruction [1, 3]. Wildfire spread prediction from the ignition point to the surrounding area is a complex phenomenon affected by multiple variables such as weather, topography, and land-cover characteristics. Traditional systems used in wildfire spread prediction are often limited in accounting for the dynamic and complex factors influencing fire behaviour. To address this challenge, this research uses a multidisciplinary approach that combines Machine Learning techniques, remote sensing data, and wildfire science to develop an advanced wildfire prediction system. This research performs a comprehensive investigation into the use of supervised classification Machine Learning(ML) models, specifically, Artificial Neural Networks(ANN), Convolutional Autoencoder, and Convolution Long Short-Term Memory(convLSTM), in predicting wildfire spread. The three ML models developed were based on domain knowledge of fire behaviour and utilised Google Earth Engine(GEE) weather data and Sentinel Hub burned scars satellite data to train a logistic method for simulating fire burn maps. The ML models were then trained, tested, and validated using this data. An analysis of the models' capabilities in predicting the propagation of wildfire spread, as well as the driving factors that affect wildfire behaviour, was performed. The following evaluation matrices: loss, precision, recall, Area Under the Curve-Precision Recall(AUC_PR) and F1 score are used to assess the performance of the models in predicting the spatial and temporal properties of wildfire spread. The results indicate that the convLSTM can predict both temporal and spatial properties of fire, while the Convolutional Autoencoder can predict only the spatial properties of fire with minimal loss. The ANN produced the least satisfying results in predicting fire's spatial properties. DA - 2025 DB - OpenUCT DP - University of Cape Town KW - Machine Learning KW - Remote Sensing KW - Satellite Data KW - Deep Learning LK - https://open.uct.ac.za PB - University of Cape Town PY - 2025 T1 - Wildfire path spread prediction system using machine learning: use of ANN, convolutional autoencoder, and ConvLSTM ML model TI - Wildfire path spread prediction system using machine learning: use of ANN, convolutional autoencoder, and ConvLSTM ML model UR - http://hdl.handle.net/11427/42379 ER - | en_ZA |
| dc.identifier.uri | http://hdl.handle.net/11427/42379 | |
| dc.identifier.vancouvercitation | Makhaba LM. Wildfire path spread prediction system using machine learning: use of ANN, convolutional autoencoder, and ConvLSTM ML model. []. University of Cape Town ,Faculty of Engineering and the Built Environment ,Department of Electrical Engineering, 2025 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/42379 | en_ZA |
| dc.language.iso | en | |
| dc.language.rfc3066 | eng | |
| dc.publisher.department | Department of Electrical Engineering | |
| dc.publisher.faculty | Faculty of Engineering and the Built Environment | |
| dc.publisher.institution | University of Cape Town | |
| dc.subject | Machine Learning | |
| dc.subject | Remote Sensing | |
| dc.subject | Satellite Data | |
| dc.subject | Deep Learning | |
| dc.title | Wildfire path spread prediction system using machine learning: use of ANN, convolutional autoencoder, and ConvLSTM ML model | |
| dc.type | Thesis / Dissertation | |
| dc.type.qualificationlevel | Masters | |
| dc.type.qualificationlevel | MSc |