Development of a Binary Model for Evaluating Water Distribution Systems by a Pressure Driven Analysis (PDA) Approach

Abstract

Investigation of Water Distribution Networks (WDNs) is considered a challenging task due to the unpredicted and uncertain conditions in water engineering. When in a WDN, a pipe failure occurs, and shut-off valves to isolate the broken pipe to allow repairing works are activated. In these new conditions, the hydraulic parameters in the network are modified because the topology of the entire system changes. If the head becomes inadequate, the Pressure Driven Analysis (PDA) is the correct approach to evaluate the performance of water networks. Hence, in the present study, the water distribution system was evaluated in pressure-driven conditions for 100 different scenarios and then using a type of neural network called Group Method of Data Handling (GMDH) as a stochastic technique. For this purpose, several most notable parameters including the base demand, pressure, and alpha (the percentage of effective supplied flow) were calculated using simulations based on a PDA approach and applied to the water distribution network of Praia a Mare in Southern Italy. In the second stage, the output parameters were used in a developed binary classification model. Finally, the obtained results showed that the GMDH algorithm can be applied as a powerful tool for modeling water distribution networks.

Development of a Binary Classification Model to Assess Safety in Transportation Systems Using GMDH-Type Neural Network Algorithm

“…In this approach, polynomial neurons are produced as simple structures and added step by step and then a complex system is formed by combining these simple structures. Natural selection patterns like evolutionary algorithms and gradual model construction indicate the capability of this approach in comparison with classical regression methods in obtaining a high-order input and output relationship [85]. The Polynomial Neural Network (PNN) is known as one of the most basic and important algorithms for building a GMDH model.…”

“…Hence, in the second step, the binary classification models are constructed based on three of the most important control parameters of the algorithm, including selection pressure (SP), maximum number of layers (MNL) and maximum number of neurons in a layer (MNNL). The SP is considered equal to 0.6 based upon previous studies [85,97]. This parameter influences the sensitivity of the modeling error, which is dimensionless; while the maximum number of layers and maximum number of neurons in a layer are selected according to the experience of experts and trial and error.…”

“…In this study, 775 accident cases were accurately evaluated and recorded from the urban and rural areas of Cosenza in southern Italy, and based on the suggestion proposed in Looney's research study, 0.75 of dataset (581 cases) were selected randomly to train, and the rest (0.25 of dataset) were used to test the developed binary model [100]. As mentioned before, there are considered three control parameters for constructing models that the SP is considered equal to 0.6, based upon previous studies [85,97], and also, the values of MNL are considered 5, 10, 15, 20 and 30 and the values of MNNL include 5, 10, 20 and 30, hence, a total of 20 models were constructed for forecasting the number of vehicles. The obtained results of 20 models are shown in Table 3.…”

Assessing the System Vibration of Circular Sawing Machine in Carbonate Rock Sawing Process Using Experimental Study and Machine Learning

“…Artificial intelligence techniques are one of the most popular ways to solve complex problems in industry and economics sectors (Mahdevari et al 2017 Naderpour et al 2019;Zhang and Geem 2019;Kayabekir et al 2020;Daneshvar and Behnood 2020;Guido et al 2020a;Kandiri et al 2020). In recent years, several studies have been conducted on the application of artificial intelligence in engineering problems (Geem and Kim 2018;Mikaeil et al 2018cMikaeil et al , 2019bSalemi et al 2018;Gnawali et al 2019;Park et al 2020;Shaffiee Haghshenas et al 2020;Noori et al 2020;Fiorini Morosini et al 2020;Guido et al 2020b). One of the most efficient methods of artificial intelligence is the imperialist competitive algorithm (ICA), suggested by Atashpaz-Gargari and Lucas (Khabbazi et al 2009;Haghshenas et al 2017).…”

Feasibility of Stochastic Models for Evaluation of Potential Factors for Safety: A Case Study in Southern Italy

“…Hence, using artificial intelligence techniques is imperative [35][36][37][38][39][40][41]. Metaheuristic algorithms, a branch of artificial intelligence techniques, are the precise scientific tools to solve uncertain systems in a wide range of sciences and industries [42][43][44][45][46][47][48][49]. Particle swarm optimization (PSO) is one of the notable metaheuristic algorithms that is appropriate for dealing with nonlinear relationships and complex problems.…”