A Greedy Sampling Design Algorithm for the Modal Calibration of Nodal Demand in Water Distribution Systems

Abstract

This paper presents a greedy optimization algorithm for sampling design to calibrate WDS hydraulic model. The proposed approach starts from the existing sensors and sequentially adds one new sensor at each optimization simulation step. In each step, the algorithm tries to minimize the calibration prediction uncertainty. The new sensor is installed in the location where the uncertainty is greatest but also sensitive to other nodes. The robustness of the proposed approach is tested under different spatial and temporal demand distribution. We found that both the number of sensors and the perturbation ratio affect the calibration accuracy as defined by the average nodal pressure deviation itself and its variability. The plot of the calibration accuracy versus the number of sensors can reasonably guide the trade-off between model calibration accuracy and number of sensors placed or the cost. This proposed approach is superior in calibration accuracy and modeling efficiency when compared to the standard genetic algorithm (SGA) and Monte Carlo Sampling algorithm (MCS).

Perbandingan Algoritma Welch Powell dan Algoritma Greedy dalam Optimasi Penjadwalan Ruang Kuliah Semester Genap Fakultas Teknik

“…Algoritma Greedy biasanya digunakan untuk menentukan pilihan terbaik dalam setiap situasi permasalahan tertentu. Secara umum, algoritma ini bekerja dengan cara mengurutkan terlebih dahulu derajat setiap simpul, kemudian menambahkan satu simpul baru di setiap langkahnya [14]. Penambahan pada setiap simpul baru ini dilakukan dengan mengambil simpul dengan derajat terkecil yang dimasukkan ke dalam graf [15].…”