Forest fire prediction and management is a worldwide concern that aims to reduce and limit fire
occurrence and caused damage. These domains gained lately important attention in Lebanon due to the high percentage of fires across the Lebanese forests. It was reported that about 95% of forest fires in Lebanon were deliberated due to human-related induced factors and hence necessary actions are demanded. To solve this problematic several studies have been conducted in order to develop a fire danger meter, based on meteorological and topographic parameters, which measures the risk of having a fire. Sequentially this fire danger risk meter is used to predict when and where a forest fire is highly expected to happen. Following our previous work where a hybrid fire danger risk meter is developed and optimized to the Lebanese forests nature, we develop in this paper a set of actions that are necessary to reduce the fire danger risk. Fire danger index values are first quantified into 6 levels with increasing danger rating. Next algorithmic proactive actions are developed that serves as a first-level fire preventive measures. These preliminary actions constitute a danger-level specific protocol and a first action trigger necessary to anticipate significant fire activity. The proposed actions are optimized to the Lebanese forest nature and following recommendations observed from forest fire cases in Lebanon.