Assessing the failures in water distribution networks using a combination of geographic information system, EPANET 2, and descriptive statistical analysis: a case study

Nowadays, issues related water are considered one of the most significant and vital problems in human societies. One of the fundamental branches of the water crisis is the distribution network-related problems. The present study attempted to extract, classify and verify the failure data obtained from the Preventive Maintenance database of Birjand Water Distribution Network (WDN). The WDN was meshed in terms of the rate of failures using a combination of Geographic Information Systems (GIS) and EPANET 2. Then it was assessed by using the descriptive statistical analysis method. Investigations revealed that the middle sections of WDN involved the highest rate of failures. Furthermore, the investigations demonstrated that from 2011 to 2016 the highest rate of failures per kilometer per year ranged in range of 0.6 to 1.8 which involved the highest aggregation for all cells. In this regard, the highest intensity aggregation of occurrence rate in 2011 was estimated as 1.4 to 1.8, while in 2012, 2013, and 2016 it was estimated as 1-1.4 and finally in 2014 and 2015, estimations showed an interval of 0.6-1

A Sustainable Decision Support System for Drinking Water Systems: Resiliency Improvement against Cyanide Contamination

Maintaining drinking water quality is considered important in building sustainable cities and societies. On the other hand, water insecurity is an obstacle to achieving sustainable development goals based on the issues of threatening human health and well-being and global peace. One of the dangers threatening water sources is cyanide contamination due to industrial wastewater leakage or sabotage. The present study investigates and provides potential strategies to remove cyanide contamination by chlorination. In this regard, the main novelty is to propose a sustainable decision support system for the dirking water system in a case study in Iran. First, three scenarios have been defined with low ([CN−] = 2.5 mg L−1), medium ([CN−] = 5 mg L−1), and high ([CN−] = 7.5 mg L−1) levels of contamination. Then, the optimal chlorine dosage has been suggested as 2.9 mg L−1, 4.7 mg L−1, and 6.1 mg L−1, respectively, for these three scenarios. In the next step, the residual cyanide was modelled with mathematical approaches, which revealed that the Gaussian distribution has the best performance accordingly. The main methodology was developing a hybrid approach based on the Gaussian model and the genetic algorithm. The outcomes of statistical evaluations illustrated that both injected chlorine and initial cyanide load have the greatest effects on residual cyanide ions. Finally, the proposed hybrid algorithm is characterized by the multilayer perceptron algorithm, which can forecast residual cyanide anion with a regression coefficient greater than 0.99 as a soft sensor. The output can demonstrate a strong positive relationship between residual cyanide- (RCN−) and injected chlorine. The main finding is that the proposed sustainable decision support system with our hybrid algorithm improves the resiliency levels of the considered drinking water system against cyanide treatments

A risk-based soft sensor for failure rate monitoring in water distribution network via adaptive neuro-fuzzy interference systems

Abstract Water Distribution Networks (WDNs) are considered one of the most important water infrastructures, and their study is of great importance. In the meantime, it seems necessary to investigate the factors involved in the failure of the urban water distribution network to optimally manage water resources and the environment. This study investigated the impact of influential factors on the failure rate of the water distribution network in Birjand, Iran. The outcomes can be considered a case study, with the possibility of extending to any similar city worldwide. The soft sensor based on the Adaptive Neuro-Fuzzy Inference System (ANFIS) was implemented to predict the failure rate based on effective features. Finally, the WDN was assessed using the Failure Modes and Effects Analysis (FMEA) technique. The results showed that pipe diameter, pipe material, and water pressure are the most influential factors. Besides, polyethylene pipes have failure rates four times higher than asbestos-cement pipes. Moreover, the failure rate is directly proportional to water pressure but inversely related to the pipe diameter. Finally, the FMEA analysis based on the knowledge management technique demonstrated that pressure management in WDNs is the main policy for risk reduction of leakage and failure