# A Brief Review of Machine Learning Algorithms in Forest Fires Science
## 4 Discussion  
In this section, we discuss some suggestions and significant challenges of integrating ML and forest fire science, as well as identify several research priorities for the future. The benefits of powerful, efficient ML methods in forest fire science and management are widely anticipated. We examined some considerations for using ML methods in forest fires in our review of studies, including data considerations, model selection, and accuracy, and which forest fire domains have been used. To develop forest fire resilience, it is essential to mention that big data measurement and analysis connected to fire occurrences and forecast of fire events require a more robust framework involving the government and populations living nearby fire-prone expanses. More research is needed to investigate such frameworks based on community social media interactions and crowdsourced event sensing. Cloud computing platforms have been developed to provide computational and data storage resources to cope with these massive datasets, particularly remote sensing big data, which have caused significant issues. The large volume, high spatial-temporal resolution, and complexity cause these problems. In any case, data processing and administration are critical in making use of substantial geographic datasets. Google Earth Engine (GEE) is one of the most promising and practical solutions for analyzing remote sensing big data. It provides access to the most freely available, multi-temporal remote sensing data, and offers scalable, cloud-based computational power for geospatial data analysis. These remote sensing data can be imported and processed rapidly on the cloud platform, eliminating the requirement to download data to local computers for processing [101; 102]. GEE provides various RS algorithms for image enhancement, image classification, and cloud masking. These algorithms can be used to improve the quality of images by reducing noise or improving accuracy. These algorithms are easily access customizable and accessible and enable data processing and visualization at various scales via JavaScript or Python Application Program Interfaces (APIs) [103; 104; 105; 106]. These capabilities eliminate most of the time-consuming preparation processes required in traditional RS techniques. ML is a data-centric modeling paradigm that can be used to detect patterns in data. It is best suited for problems with sufficient high-quality data. However, this tool only comes into play when the situation calls for it, which is not always the case. To address the fundamental issue of data scarcity while eliminating human error, we might construct new synthetic data instances where training a forest fire detection model with synthetic datasets enhances model performance [107]. In forest fire domains, where most ML applications use some type of imaging, remote sensing plays a crucial role in data collection [108]. Although continued advances in remote sensing have increased the availability of large spatiotemporal datasets, not all satellite images have good resolution. In addition, weather may not be stable in all situations as it varies, resulting in noisy images. UAVs can collect high-resolution photos of the forest. They can give additional frequent and precise pictures of the forest canopy than ground-based imaging due to their excellent mobility and ability to cover large regions at minimal expense [109]. As a result, more accurate fire location detection is achievable than satellite photography. As a result, the combination of UAVs and machine learning could be highly effective for detecting forest fires in their early stages. It can also be beneficial for transmitting critical information to relevant authorities via efficient communication technology [110]. These features of UAVs make them effective as a solution for the real-time detection of forest fires utilizing UAV footage in various light and weather conditions.