Wildfire is an environmental hazard that has both local and global effects, causing economic losses and various severe environmental problems. Due to the adverse effects of climate changes and anthropogenic activities, wildfire is anticipated more frequent and extreme; therefore, new and more efficient tools for forest fire prevention and control are essential. This study proposes a new deep neural computing approach for spatial prediction of wildfire in a tropical climate area. For this purpose, deep neural computing (Deep-NC) with a structure of 3 hidden layers was proposed. The Rectified Linear Unit (ReLU) activation function was adopted to infer wildfire dangers from the input factors. To search and optimize the weights of the model, Stochastic Gradient Descent (SGD), Root Mean Square Propagation (RMSProp), Adaptive Moment Estimation (Adam), and Adadelta optimizers were employed. Also, this study has established a Geographic Information System (GIS) database for Gia Lai province (Vietnam) to train and verify the newly developed deep computing approach. The twelve ignition factors, namely, slope, aspect, elevation, curvature, land use, NVDI, NDWI, NDMI, temperature, wind speed, relative humidity, and rainfall, have been used to characterize the study area with respect to forest fire susceptibility. According to experimental results, the Adam optimized Deep-NC model delivered the highest predictive accuracy (AUC = 0.894, Kappa = 0.63). Accordingly, this model has been employed to establish a forest fire susceptibility map for Gia Lai province. The proposed Deep-NC model and the newly constructed forest fire susceptibility map can help local authorities in land use planning and hazard mitigation/prevention.

野火是一种对当地和全球都有影响的环境危害，会造成经济损失和各种严重的环境问题。由于气候变化和人为活动的不利影响，预计野火会更加频繁和极端。因此，对于森林火灾的预防和控制，必须使用新的，更有效的工具。这项研究提出了一种新的深度神经计算方法，用于在热带气候区进行野火的空间预测。为此，提出了具有3个隐藏层的结构的深度神经计算（Deep-NC）。采用整流线性单元（ReLU）激活功能从输入因素推断野火危险。要搜索和优化模型的权重，随机梯度下降（SGD），均方根传播（RMSProp），自适应矩估计（Adam），和Adadelta优化器被采用。此外，本研究还为嘉来省（越南）建立了地理信息系统（GIS）数据库，以训练和验证新开发的深度计算方法。十二种着火因子，即坡度，纵横比，高程，曲率，土地利用，NVDI，NDWI，NDMI，温度，风速，相对湿度和降雨，已被用来表征研究区域的森林火灾敏感性。根据实验结果，Adam优化的Deep-NC模型提供了最高的预测精度（AUC = 0.894，Kappa = 0.63）。因此，该模型已被用来为嘉来省建立森林火灾敏感性图。