Developing a methodology for early leakage detection in landfills: application of the fuzzy transformation technique and probabilistic artificial neural networks

In this paper, a new methodology is developed for early detecting leakage from landfills to groundwater systems. The optimal number and locations of monitoring wells are determined with respect to maximizing the probability of detecting a pollution source and minimizing the total cost of monitoring system. The concentration of a water quality indicator is simulated by analytically solving the mass transport equations in porous media. In the developed methodology, the uncertainties associated with the leakage characteristics as well as parameters related to pollution mass transport equations are addressed using the fuzzy set theory. Also, a fuzzy transformation technique is utilized, which decomposes the fuzzy membership functions into a number of intervals called $\alpha$-cuts. Corresponding to each leakage scenario, fuzzy membership function of the concentration of the water quality indicator at each monitoring well is obtained using the fuzzy transformation technique. The resulted fuzzy numbers are compared with a fuzzy threshold for detecting pollution using a fuzzy ranking technique. Finally, the optimum number of monitoring wells with their best locations can be determined using a trade-off curve between the probability of pollution detection and the number of monitoring wells. For real-time operation of the monitoring system, a probabilistic artificial neural network is trained and verified which provides the probability of occurring leakage at each section of the landfill based on the concentration of the water quality indicator at the monitoring wells. The applicability and effectiveness of the proposed methodology in detecting pollution leakage from landfills are examined by applying it to a case study in Iran.