Using adaptive neuro-fuzzy inference system and imperialist competitive algorithm for leak detection in pipe networks

•Proposed a technique to reduce search space for detecting leakage locations.•Developed a hybrid model for Leak Detection in Pipe Networks.•Used Neuro Fuzzy Inference System (ANFIS) and Imperialist Competitive Algorithm (ICA).•The predicted leakage locations by our proposed method match the actual points.•The final normalized predicted leakage values have a maximum error rate of 10%. Finding the position and quantity of leakage in water distribution networks (WDNs) is challenging in cities with old water pipelines. Previous studies proposed physical and data-driven techniques to find the location and quantity of leakage in WDNs. Most approaches rely on large sample of measurements from the WDN which makes them impractical. We propose an efficient model incorporating Adaptive Neuro-Fuzzy Inference System (ANFIS) and Imperialist Competitive Algorithm (ICA) techniques, to reduce the number of required samples. ANFIS approximates the locations and quantities of leakage, while the ICA corrects its estimations. Due to nonlinearity, the application of ICA alone results in long run times, while ANFIS reduces the number of decision variables for ICA, and hence the convergence rate improves. In other words, we reduce the search space leading to reduced computational time and improved accuracy. Results show that the normalized predicted leakage values had a maximum error rate of 10%.