Understanding the climatic conditions in underground mines is necessary for efficient ventilation design, cost savings, and to ensure the health and safety of mine workers. Large volumes of ventilation and climatic data including air volume, barometric pressure, dry bulb temperature, and relative humidity were collected at active underground precious metal mines in Nevada, which allows for the determination of wet bulb temperature and other key parameters. Through the utilization of neural networks, the wet bulb temperature at the bottom of the intake shafts is predicted, while taking into account the “thermal flywheel effect” (TFE). Wet bulb temperature is one of the most important climatic parameters to model and understand because it significantly affects the work conditions and the cooling capacity of the ventilating air. The accurate prediction of the dry bulb and the wet bulb temperatures at the bottom of intake shafts is critical when assessing the climatic conditions in future underground mines and deciding on whether a cooling system is needed to assure adequate working conditions throughout the mine. By utilizing accurate predictions of wet bulb temperatures and other climatic parameters, mine personnel will be safer as reported by Bluhm et al. (2003), and a more accurate ventilation design can be achieved resulting in major cost savings for underground mines.

了解地下矿山的气候条件对于有效的通风设计，节省成本以及确保矿山工人的健康和安全是必不可少的。在内华达州活跃的地下贵重金属矿山收集了大量的通风和气候数据，包括风量，大气压力，干球温度和相对湿度，从而可以确定湿球温度和其他关键参数。通过利用神经网络，可以在考虑“热飞轮效应”（TFE）的情况下预测进气轴底部的湿球温度。湿球温度是建模和理解的最重要的气候参数之一，因为它会显着影响工作条件和通风空气的冷却能力。在评估未来地下矿山的气候条件并确定是否需要冷却系统以确保整个矿山正常工作时，准确预测进气井底部的干球和湿球温度至关重要。通过利用湿球温度和其他气候参数的准确预测，矿工人员将更加安全，如Bluhm等人报道的那样。（2003年），可以实现更准确的通风设计，从而为地下矿井节省大量成本。通过利用湿球温度和其他气候参数的准确预测，矿工人员将更加安全，如Bluhm等人报道的那样。（2003年），可以实现更准确的通风设计，从而为地下矿井节省大量成本。通过利用湿球温度和其他气候参数的准确预测，矿工人员将更加安全，如Bluhm等人报道的那样。（2003年），可以实现更准确的通风设计，从而为地下矿井节省大量成本。