Resilience-based performance assessment of water-recycling schemes in urban water systems

Article16th Water Distribution System Analysis Conference, WDSA2014 — Urban Water Hydroinformatics and Strategic PlanningWater reuse schemes in urban water system are assessed in this paper against a number of hydraulic performance indicators. A city metabolism model, WaterMet2, is used to evaluate the performance of water reuse schemes. A multi-objective evolutionary algorithm is employed to identify Pareto optimal solutions for the following three objectives: resilience, reliability and total cost. The demonstration of the suggested approach on a real-world case study show the importance of using the resilience index for determining the appropriate schemes. The results suggest, in the case analysed here, the rainwater-harvesting scheme plays a significant role for improvement of resilience index.This work was carried out as part of the ‘TRansition to Urban water Services of Tomorrow’ (TRUST) project. The authors wish to acknowledge the European Commission for funding TRUST project in the 7th Framework Programme under Grant Agreement No. 265122

Pipeline failure prediction in water distribution networks using evolutionary polynomial regression combined with Κ- means clustering

This is the author accepted manuscript. The final version is available from Taylor & Francis via the DOI in this record.This paper presents a new approach for improving pipeline failure predictions by combining a data driven statistical model, i.e. Evolutionary Polynomial Regression (EPR), with K-means clustering. The EPR is used for prediction of pipe failures based on length, diameter and age of pipes as explanatory factors. Individual pipes are aggregated using their attributes of age, diameter and soil type to create homogenous groups of pipes. The created groups were divided into training and test datasets using the cross-validation technique for calibration and validation purposes respectively. The K-means clustering is employed to partition the training data into a number of clusters for individual EPR models. The proposed approach was demonstrated by application to the cast iron pipes of a water distribution network in the UK. Results show the proposed approach is able to significantly reduce the error of pipe failure predictions especially in the case of a large number of failures. The prediction models were used to calculate the failure rate of individual pipes for rehabilitation planning.The work reported is supported by the UK Engineering & Physical Sciences Research Council (EPSRC) project Safe &SuRe (EP/K006924/1)

Event-based decision support algorithm for real-time flood forecasting in urban drainage systems using machine learning modelling

Urban flooding is a major problem for cities around the world, with significant socio-economic consequences. Conventional real-time flood forecasting models rely on continuous time-series data and often have limited accuracy, especially for longer lead times than 2 hrs. This study proposes a novel event-based decision support algorithm for real-time flood forecasting using event-based data identification, event-based dataset generation, and a real-time decision tree flowchart using machine learning models. The results of applying the framework to a real-world case study demonstrate higher accuracy in forecasting water level rise, especially for longer lead times (e.g., 2–3 hrs), compared to traditional models. The proposed framework reduces root mean square error by 50%, increases accuracy of flood forecasting by 50%, and improves normalised Nash–Sutcliffe error by 20%. The proposed event-based dataset framework can significantly enhance the accuracy of flood forecasting, reducing the occurrences of both false alarms and flood missing and improving emergency response systems

Developing a smart and clean technology for bioremediation of antibiotic contamination in arable lands

This study presents a smart technological framework to efficiently remove azithromycin from natural soil resources using bioremediation techniques. The framework consists of several modules, each with different models such as Penicillium Simplicissimum (PS) bioactivity, soft computing models, statistical optimisation, Machine Learning (ML) algorithms, and Decision Tree (DT) control system based on Removal Percentage (RP). The first module involves designing experiments using a literature review and the Taguchi Orthogonal design method for cultural conditions. The RP is predicted as a function of cultural parameters using Response Surface Methodology (RSM) and three ML algorithms: Instance-Based K (IBK), KStar, and Locally Weighted Learning (LWL). The sensitivity analysis shows that pH is the most important factor among all parameters, including pH, Aeration Intensity (AI), Temperature, Microbial/Food (M/F) ratio, and Retention Time (RT), with a p-value of <0.0001. AI is the next most significant parameter, also with a p-value of <0.0001. The optimal biological conditions for removing azithromycin from soil resources are a temperature of 32 °C, pH of 5.5, M/F ratio of 1.59 mg/g, and AI of 8.59 m3/h. During the 100-day bioremediation process, RP was found to be an insignificant factor for more than 25 days, which simplifies the conditions. Among the ML algorithms, the IBK model provided the most accurate prediction of RT, with a correlation coefficient of over 95%

Algal proliferation risk assessment using Vine Copula-based coupling methods in the South-to-North Water Diversion Project of China

The Middle Route of the South-to-North Water Diversion Project of China (MRSNWDPC), i.e., the longest inter-basin water diversion project (1,432 km) in the world, has delivered more than 60 billion m3 of water resources to North China and benefiting more than 100 million people since December 2014. However, the abnormal algal proliferation in the main canal under low nutrient background has seriously threatened the water quality safety of this mega project. In this research, 3 years of monitoring data matrix, including water temperature (WT), flow discharge (Q), flow velocity (V), dissolved oxygen (DO), and the algal cell density (ACD), from the main canal of the MRSNWDPC were analyzed. The nonlinear relationships were determined based on multiple regression models, and a composite risk analysis model was constructed by Latin hypercube sampling (LHS) method coupled with Vine Copula function. The impacts of different hydrological and environmental factors on algal proliferation were comprehensively analyzed by Bayesian theory. The results showed that the WT gradually decreased from upstream to downstream, with a narrow range of 16.6–17.4°C, and the annual average concentrations of DO showed a gradual increase from upstream to downstream. The flow velocity of MRSNWDPC had a tendency to increase year by year, and the maximum flow velocity exceeds 0.8 m/s upstream, midstream and downstream by 2018. The ACD accumulated along the main canal, and the annual average ACDs of downstream were the highest, ranging from 366.17 to 462.95 × 104 cells/L. The joint early-warning method considering both water temperature and flow velocity conditions is an effective way for algal proliferation risk warning management. When water temperatures of the upstream, midstream, and downstream were below 26, 26, and 23°C, respectively, the algal proliferation risk can be controlled under 50% by the flow velocity at 0.3 m/s; otherwise, the flow velocity needs to be regulated higher than 0.8 m/s. In order to keep the midstream and downstream avoid abnormal algal proliferation events (ACD ≥ 500 × 104 cells/L), the corresponding ACDs of the upstream and midstream need to be controlled lower than 319 × 104 cells/L and 470 × 104 cells/L, respectively. This study provides a scientific reference for the long-distance water diversion project’s algal control and environmental protection. The proposed coupling Vine Copula models can also be widely applied to multivariate risk analysis fields

Enhancing community resilience in arid regions: A smart framework for flash flood risk assessment

This paper presents a novel framework for smart integrated risk management in arid regions. The framework combines flash flood modelling, statistical methods, artificial intelligence (AI), geographic evaluations, risk analysis, and decision-making modules to enhance community resilience. Flash flood is simulated by using Watershed Modelling System (WMS). Statistical methods are also used to trim outlier data from physical systems and climatic data. Furthermore, three AI methods, including Support Vector Machine (SVM), Artificial Neural Network (ANN), and Nearest Neighbours Classification (NNC), are used to predict and classify flash flood occurrences. Geographic Information System (GIS) is also utilised to assess potential risks in vulnerable regions, together with Failure Mode and Effects Analysis (FMEA) and Hazard and Operability Study (HAZOP) methods. The decision-making module employs the Classic Delphi technique to classify the appropriate solutions for flood risk control. The methodology is demonstrated by its application to the real case study of the Khosf region in Iran, which suffers from both drought and severe floods simultaneously, exacerbated by recent climate changes. The results show high Coefficient of determination (R2) scores for the three AI methods, with SVM at 0.88, ANN at 0.79, and NNC at 0.89. FMEA results indicate that over 50% of scenarios are at high flood risk, while HAZOP indicates 30% of scenarios with the same risk rate. Additionally, peak flows of over 24 m3/s are considered flood occurrences that can cause financial damage in all scenarios and risk techniques of the case study. Finally, our research findings indicate a practical decision support system that is compatible with sustainable development concepts and can enhance community resilience in arid regions

Urban water system metabolism assessment using WaterMet2 model

12th International Conference on Computing and Control for the Water Industry, CCWI2013, 2013-09-06, 2013-09-09, Perugia, ItalyThis paper presents a new "WaterMet2" model for integrated modelling of an urban water system (UWS). The model is able to quantify the principal water flows and other main fluxes in the UWS. The UWS in WaterMet2 is characterised using four different spatial scales (indoor area, local area, subcatchment and system area) and a daily temporal resolution. The main subsystems in WaterMet2 include water supply, water demand, wastewater and cyclic water recovery. The WaterMet2 is demonstrated here through modelling of the urban water system of Oslo city in Norway. Given a fast population growth, WaterMet2 analyses a range of alternative intervention strategies including 'business as usual', addition of new water resources, increased rehabilitation rates and water demand schemes to improve the performance of the Oslo UWS. The resulting five intervention strategies were compared with respect to some major UWS performance profiles quantified by the WaterMet2 model and expert's opinions. The results demonstrate how an integrated modelling approach can assist planners in defining a better intervention strategy in the future.This work was carried out as part of the ‘TRansition to Urban water Services of Tomorrow’ (TRUST) project. The authors wish to acknowledge the European Commission for funding TRUST project in the 7th Framework Programme under Grant Agreement No. 265122

Optimal Rehabilitation of Water Distribution Systems using a Cluster-based Technique

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Optimal rehabilitation of large water distribution system (WDS) with many decision variables, is often time-consuming and computationally expensive. This paper presents a new optimal rehabilitation methodology for WDSs based on graph theory clustering concept. The methodology starts with partitioning the WDS based on its connectivity properties into a number of clusters (small sub-systems). Pipes which might have direct impact on system performance are identified and considered for rehabilitation problem. Three optimisation-based strategies are then considered for pipe rehabilitation in the clustered network: 1) rehabilitation of some of the pipes inside the clusters; 2) rehabilitation of pipes in the path supplying water to the clusters; 3) combination of strategies 1 and 2. In all optimisation strategies, the decision variables for rehabilitation problem are the diameters of duplicated pipes; the objective functions are to minimise the total cost of duplicated pipes and to minimise the number of nodes with pressure deficiency. The performance of proposed strategies was demonstrated in a large WDS with pressure deficiencies. The performance of these strategies were also compared to the full search space optimisation strategy and engineering judgement based optimisation strategy in which all pipes and selection of pipes are considered as decision variables respectively. The results show that strategy 3 is able to generate solutions with similar performance that are cheaper by around 53% and 35% in comparison with the full search space and engineering judgement based optimisation strategies respectively. The results also demonstrate that the cluster-based approach can reduce the computational efforts for achieving optimum solutions compared to the other optimization strategies

Performance assessment of water reuse strategies using integrated framework of urban water metabolism and water-energy-pollution nexus

This paper evaluates the metabolism-based performance of a number of centralised and decentralised water reuse strategies and their impact on integrated urban water systems (UWS) based on the nexus of water-energy-pollution. The performance assessment is based on a comprehensive and quantitative framework of urban water metabolism developed for integrated UWS over a long-term planning horizon. UWS performance is quantified based on the tracking down of mass balance flows/fluxes of water, energy, materials, costs, pollutants, and other environmental impacts using the WaterMet2 tool. The assessment framework is defined as a set of key performance indicators (KPIs) within the context of the water-energy-pollution nexus. The strategies comprise six decentralised water reuse configurations (greywater or domestic wastewater) and three centralised ones, all within three proportions of adoption by domestic users (i.e. 20, 50, and 100%). This methodology was demonstrated in the real-world case study of San Francisco del Rincon and Purisima del Rincon cities in Mexico. The results indicate that decentralised water reuse strategies using domestic wastewater can provide the best performance in the UWS with respect to water conservation, green house gas (GHG) emissions, and eutrophication indicators, while energy saving is almost negligible. On the other hand, centralised strategies can achieve the best performance for energy saving among the water reuse strategies. The results also show metabolism performance assessment in a complex system such as integrated UWS can reveal the magnitude of the interactions between the nexus elements (i.e. water, energy, and pollution). In addition, it can also reveal any unexpected influences of these elements that might exist between the UWS components and overall system