ANALYTICAL STUDY OF FUZZY-BASED TRIANGULAR MEMBERSHIP FUNCTIONS COMBINED WITH REGRESSION FOR COMPOST QUALITY ASSESSMENT

The municipal solid waste compost consists of elements with a varied composition, including light and heavy metal elements. For MSW compost to act as a soil conditioner, and to ensure agricultural stakeholders to believe in its use for crops production, validation of elements is obligatory. The triangular membership function evaluates each element of a fuzzy set for both discrete and continuous values, and regression analysis estimates the relationship between values. In this paper, a triangular membership function (µf) is studied and used to characterize the effect of individual elements available in the compost sample. The characterization determines the variation in the composition of elements in the compost sample and accordingly calculates its scorei. Furthermore, a reinvestigation is done by applying multiple regression analysis, especially on heavy metals, to compare their composition with light mineral nutrients and other supplementary elements. A relationship between R=4.12 and R2=0.067498635 is derived to determine the predicted value and defines the composition of heavy metals as attributed to another mineral nutrients. Furthermore, a correlation (Co) is derived to find the performance of the compost sample to decide whether both light and supplementary mineral nutrients are capable of minimizing the effect of heavy metals. A gratuity score (Gsi) is added to each heavy metal depending on the correlation value to form a composti. The scorei=88.11 and composti = 9.12 obtained, was summated to derive Ci=97.23, stating that the increase in score value declares that the compost sample is mature enough to be used for agriculture and enhance crops productivity.