{'fraction_non_alphanumeric': 0.027472527472527472, 'fraction_numerical': 0.02197802197802198, 'mean_word_length': 5.345844504021447, '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}}
A Fuzzy Water Quality Index for Watershed Quality Analysis and Management

Climate change and hydric stress are limiting the availability of clean water. Overexploitation of natural resources has led to environmental unbalance. Present decisions relative to the management of hydric resources will deeply affect the economy and our future environment. The use of indicators is a good alternative for the evaluation of environmental behavior as well as a management instrument, as long as the conceptual and structural parameters of the indicators are respected. The use of fuzzy logic to study the influence and the consequences of environmental problems has increased significantly in recent years. According to Silvert (1997), most activities, either natural of anthropic, have multiple effects and any environmental index should offer a consistent meaning as well as a coherent quantitative and qualitative appraisal of all these effects. Among the several reasons for applying fuzzy logic to complex situations, the most important is probably the need to combine different indicators. Maybe the most significant advantage of the use of fuzzy logic for the development of environmental indicators is that it combines different aspects with much more flexibility than other methods, such as, for example, binary indices of the kind “acceptable vs. unacceptable.” Methods to integrate several variables related to water quality in a specific index are increasingly needed in national and international scenarios. Several authors have integrated water quality variables into indices, technically called Water Quality Indices (WQIs) (Bolton et al., 1978; Bhargava, 1983; House, 1989; Mitchell, 1996; Pesce and Wunderlin, 1999; Cude, 2001; Liou et al., 2004; Said et al., 2004; Silva and Jardim, 2006; Nasiri et al., 2007). Most are based in a concept developed by the U. S. National Sanitation Foundation (NSF, 2007). There is an obvious need for more advanced techniques to assess the importance of water quality variables and to integrate the distinct parameters involved. In this context, new, alternative integration methods are being developed. Artificial Intelligence has thus become a tool for modeling water quality (Chau, 2006). Traditional methodologies cannot classify and quantify environmental effects of a subjective nature or even provide formalism for