# Applications of Artificial Intelligence in Fire Safety of Agricultural Structures
## 2 Fire Safety in Agricultural Structures/Farms
### Artificial-Intelligence-Based Networks for Fire Prevention/Monitoring/Simulation
The correlation between fire hazards and the moisture content in silos is depicted in Figure 2. Beyond grain-moisture-specific fire hazards, there are varied concerns about the impact of a different confounding factor. The severity of the fire hazard is predicted by the effect of wind on the fire target and doorway pressure, velocity, and temperature [50]. In other cases [51], the maximum concentration of oxygen (MOC) has been proven to predict the risk of a fire hazard in grain silos. The drawbacks and context-specific benefits associated with specific AI networks informed the development of alternative models, including the radial basis function neural network (RBFNN) and the fuzzy logic inference system [47], to complement the traditional backpropagation neural network (BPNN). BPNN systems suffer from a myriad of challenges, including poor convergence and learning speed [47] and the inability to integrate prior knowledge/experience. [PERSON] and [PERSON] [47] utilized the technique and established that RBFNN systems provide a reliable and intelligent assessment system for fire safety. Even though the outcomes were applicable to high-rise buildings [47], they were relevant to multistorey silo structures, prone to fires associated with postharvest anaerobic fermentation and aerobic respiration of grains [23]. The practical utility of the systems was linked to the adaptive fuzzy-RBFNN's high efficiency, accuracy, and reliability. However, there are practical constraints to broad commercial application, including limited application in fire safety assessment [47] and the fact that the simulated findings are specific to high-rise structures. The case for a fuzzy logic system made by [PERSON] and [PERSON] is further supported by [PERSON] et al. [30]. In the latter study, the DFLER-fuzzy-based system was proven useful in wireless sensor networks integrated with temperature and smoke sensors. The network's ability to detect fires accurately was grounded on the following considerations. First, the integration of additional sensors (fusion of temperature, ionization, photoelectric, and CO sensors) translates to better detection capabilities. The benefits attributed to the fusion of different sensors by [PERSON] et al. [30] were collaborated by [PERSON] et al. [52], who employed  
Figure 2: Relationship between the moisture content in silos and fire hazards [23]. deep-learning-based detectors coupled with IoT sensors for active fire location and real-time monitoring. Second, the fuzzy systems that incorporated the AI approach featured intelligent detection algorithms that facilitate autonomy, where each sensor node is capable of detecting fire on its own. Such capabilities are necessary, considering that fires follow a distinct pattern. The multisensor approach introduces new constraints for farm-related applications, where investments in nonproductive activities such as fire prevention are not a priority. From a theoretical point of view, it might be challenging for farms to invest in fuzzy systems and sensor fusion due to cost considerations. Alternatively, cost should not be regarded as impediments to investments in AI-based systems, considering advances in technology led to the development of cheap and precise networks. For example, a naive Bayes classifier-feedforward neural network (FFNN) tool was considered useful in monitoring fire risks by [PERSON] et al. [30]. The basic architecture of the FFNN system is depicted in Figure 3. It is clear that the system comprises interconnected nodes, and the back layers/input layers feed the hidden and output layers. On the downside, the potential application of the naive Bayes classifier-feedforward neural network (FFNN) tool would involve a tradeoff with other metrics of performance, including the accurate determination of weights or learning.