Análise qualitativa de água subterrânea utilizando sistema de inferência Fuzzy.

Abstract

Water is the main element of the hydrological cycle, a renewable and indispensable natural resource for human activities, but considered finite. Currently, the quality of groundwater in Brazil is constantly degrading due to the exponential increase in polluting sources. Population growth, urbanization, deficiency in the sanitary sewage system and climate change stand out as the main causes of the poor quality of water resources. Knowing the importance and lack of availability in potable form, there is an urgent need for its preservation to mitigate the effects of its scarcity. In semi-arid regions or in places with inadequate management, the conservation of this resource is a major challenge. This work deals with the development of the Groundwater Quality Index through Mamdani-type Fuzzy Inference systems – IQASF. The quality index is an important tool for interpreting qualitative data. Water quality is based on the aggregation of the values of several parameters: turbidity, total dissolved solids, pH, nitrite, nitrate, chloride, total coliforms and E.coli. The parameters were grouped by similar characteristics in three dimensions: physical, chemical, and bacteriological, which later generated the Groundwater Quality Index (IQASF). Finally, after developing the IQASF, the model was applied in a case study in Juazeiro do Norte - CE, Brazil, in 36 wells with different characteristics. The IQASF determines the raw groundwater quality of the wells, using the maximum values allowed by CONAMA Resolution No. 396/2008 as a reference, the intervals between values were established with the help of specialists. The results obtained in the application of the IQASF indicated that most of the wells have excellent quality. However, some wells have poor grade persistence, indicating the need for constant monitoring. From the diagnosis of the wells studied, the veracity of the IQASF was verified. In general, the IQASF proved to be very useful in tracking changes in water quality in each sampling site over time and can be applied to the benefit of effective management, helping decision makers.