{'fraction_non_alphanumeric': 0.01598746081504702, 'fraction_numerical': 0.0018808777429467085, 'mean_word_length': 5.318811881188119, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Analysis of Area Burned by Wildfires Through the Partitioning of a Probability Model 1

An analysis of forest fires by using a partitioned probability distribution is presented. Area burned during afire is fitted to a probability model. This model is partitioned into small, medium, and large fires. Conditional expected values are computed for each partition. Two cases are presented: the two-parameter Weibull and the Truncated Shifted Pareto probability models. The methodology allows a comparison of area burned and cost for small, medium, and large fires among different attack strategies. The partitioned functions may also be used as a basis to refocus fire management optimization with a multiple objective formulation. Introduction Fire management decisions are frequently made on the basis of many uncertain and highly variable factors, such as fire behavior, weather forecasts, fire effects, and performance of initial attack force. Although most of the modeling of these components have been deterministic, fire managers probably balance their judgments about the uncertain factors against their preferences for possible consequences or outcomes, deviating from the optimal solution provided by a decision-making model. All of these deviations from an optimum solution implicitly recognize the effect of large fires in deciding what level of effort or resources to allocate in fire management. Despite that forest fires are highly stochastic and complex events, most of the decision models that are uses are based on techniques that use the expected value of the damage function, and a few others are deterministic models. Fire management decisions obviously are influenced by the concern about the occurrence of large fires. Large fires with low probability of occurrence have a large impact in natural, social, and economic systems. However, most fires are extinguished in the initial stages and remain small. These smaller fires have a large probability of occurrence, but the resulting damage is practically negligible on an individual basis. Because of these probabilities, the expected value of a damage function misrepresents both extremes. Also, the use of expected value does not adequately represent the consequences associated with different fire management policies. Traditional decision-making and budgeting in forest fire management have been based on the postulate that resources must be allocated in proportion to the resource value to be protected. However, it has been mentioned in several forums that the high costs of fire control are not justified when a strict economic analysis is made because the optimization criterion consists largely of costs (Gale 1977). Moreover, actions that are seen to reduce the risk of the occurrence of large wildfires justify high expenditures made on those large fires, as well as those made on smaller fires. This paper presents a methodology to incorporate different damage levels in fire management decision-making by analytically incorporating large fires. The methodology partitions a probability model to describe area burned, to address different damage levels and their probability of occurrence. The assumption is 1