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

A novel approach for water quality management in water distribution systems by multi-objective booster chlorination

Copyright © 2012 International Journal of Civil EngineeringCompared to conventional chlorination methods which apply chlorine at water treatment plant, booster chlorination has almost solved the problems of high dosages of chlorine residuals near water sources and lack of chlorine residuals in the remote points of a water distribution system (WDS). However, control of trihalomethane (THM) formation as a potentially carcinogenic disinfection by-product (DBP) within a WDS has still remained as a water quality problem. This paper presents a two-phase approach of multi-objective booster disinfection in which both chlorine residuals and THM formation are concurrently optimized in a WDS. In the first phase, a booster disinfection system is formulated as a multi-objective optimization problem in which the location of booster stations is determined. The objectives are defined as to maximize the volumetric discharge with appropriate levels of disinfectant residuals throughout all demand nodes and to minimize the total mass of disinfectant applied with a specified number of booster stations. The most frequently selected locations for installing booster disinfection stations are selected for the second phase, in which another two-objective optimization problem is defined. The objectives in the second problem are to minimize the volumetric discharge avoiding THM maximum levels and to maximize the volumetric discharge with standard levels of disinfectant residuals. For each point on the resulted trade-off curve between the water quality objectives optimal scheduling of chlorination injected at each booster station is obtained. Both optimization problems used NSGA-II algorithm as a multi-objective genetic algorithm, coupled with EPANET as a hydraulic simulation model. The optimization problems are tested for different numbers of booster chlorination stations in a real case WDS. As a result, this type of multi-objective optimization model can explicitly give the decision makers the optimal location and scheduling of booster disinfection systems with respect to the trade-off between maximum safe drinking water with allowable chlorine residual levels and minimum adverse DBP levels

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%

A critical review of digital technology innovations for early warning of water-related disease outbreaks associated with climatic hazards

Water-related climatic disasters pose a significant threat to human health due to the potential of disease outbreaks, which are exacerbated by climate change. Therefore, it is crucial to predict their occurrence with sufficient lead time to allow for contingency plans to reduce risks to the population. Opportunities to address this challenge can be found in the rapid evolution of digital technologies. This study conducted a critical analysis of recent publications investigating advanced technologies and digital innovations for forecasting, alerting, and responding to water-related extreme events, particularly flooding, which is often linked to disaster-related disease outbreaks. The results indicate that certain digital innovations, such as portable and local sensors integrated with web-based platforms are new era for predicting events, developing control strategies and establishing early warning systems. Other technologies, such as augmented reality, virtual reality, and social media, can be more effective for monitoring flood spread, disseminating before/during the event information, and issuing warnings or directing emergency responses. The study also identified that the collection and translation of reliable data into information can be a major challenge for effective early warning systems and the adoption of digital innovations in disaster management. Augmented reality, and digital twin technologies should be further explored as valuable tools for better providing of communicating complex information on disaster development and response strategies to a wider range of audiences, particularly non-experts. This can help to increase community engagement in designing and operating effective early warning systems that can reduce the health impact of climatic disasters

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

Decision Support System for the Long-Term City Metabolism Planning Problem

AcceptedArticleA Decision Support System (DSS) tool for the assessment of intervention strategies (Alternatives) in an Urban Water System (UWS) with an integral simulation model called “WaterMet2” is presented. The DSS permits the user to identify one or more optimal Alternatives over a fixed long-term planning horizon using performance metrics mapped to the TRUST sustainability criteria (Alegre et al., 2012). The DSS exposes lists of in-built intervention options and system performance metrics for the user to compose new Alternatives. The quantitative metrics are calculated by the WaterMet2 model and further qualitative or user-defined metrics may be specified by the user or by external tools feeding into the DSS. A Multi-Criteria Decision Analysis (MCDA) approach is employed within the DSS to compare the defined Alternatives and to rank them with respect to a pre-specified weighting scheme for different Scenarios. Two rich, interactive Graphical User Interfaces, one desktop and one web-based, are employed to assist with guiding the end user through the stages of defining the problem, evaluating and ranking Alternatives. This mechanism provides a useful tool for decision makers to compare different strategies for the planning of UWS with respect to multiple Scenarios. The efficacy of the DSS is demonstrated on a northern European case study inspired by a real-life urban water system for a mixture of quantitative and qualitative criteria. The results demonstrate how the DSS, integrated with an UWS modelling approach, can be used to assist planners in meeting their long-term, strategic level sustainability objectives.EU 7th Framework Programm

A new multi-criteria framework to identify optimal detention ponds in urban drainage systems

Urban development broadly impacts the hydrological cycle, leading to increased peak flow and flooding. Surface water detention ponds are among the most efficient measures for attenuating peak flow and returning it from development to pre-development conditions. However, the major challenge is identifying optimal locations and cost-effective designs for these ponds. This paper presents a new framework for identifying the best strategies for using detention ponds to control floods in urban drainage systems (UDS). The framework comprises a portfolio of simulation tools coupled with evolutionary optimisation and multi-criteria decision analysis models. Hydraulic simulation of UDS is first modelled using SWMM and GIS tools. A multi-objective optimisation model was used to find the optimal location and design for detention ponds. The compromise programming (CP) multi-criteria decision-making method was then used to prioritise potential best management solutions for detention ponds based on several sustainability criteria comprising economic, environmental, physiographic and social factors. The results identified the key features of potential detention ponds appearing in all multi-objective optimal solutions that are useful for decision-makers/designers when planning/designing for new detention ponds. The selected optimal pond strategies can significantly improve the UDS performance by decreasing flood damage between 66% and 90% at the cost of between $50,000 and$160,000

Decision process in large-scale crisis management

International audienceThis paper deals with the decision-aiding process in large-scale crisis such as natural disasters. It consists in four phases: decision context characterization, system modelling, aggregation and integration. The elements of the context, such as crisis level, risk situation, decision-maker problem issue are defined through the characterization phase. At the feared event occurrence, these elements will interact on a target system. Through the model on this system, the consequences to stakes could be assessed or estimated. The presented aggregation approaches will allow taking the right decisions. The architecture of a Decision Support System is presented in the integration phase

Enhancing flood risk mitigation by advanced data-driven approach

Flood events in the Sefidrud River basin have historically caused significant damage to infrastructure, agriculture, and human settlements, highlighting the urgent need for improved flood prediction capabilities. Traditional hydrological models have shown limitations in capturing the complex, non-linear relationships inherent in flood dynamics. This study addresses these challenges by leveraging advanced machine learning techniques to develop more accurate and reliable flood estimation models for the region. The study applied Random Forest (RF), Bagging, SMOreg, Multilayer Perceptron (MLP), and Adaptive Neuro-Fuzzy Inference System (ANFIS) models using historical hydrological data spanning 50 years. The methods involved splitting the data into training (50–70 %) and validation sets, processed using WEKA 3.9 software. The evaluation revealed that the nonlinear ensemble RF model achieved the highest accuracy with a correlation of 0.868 and an root mean squared error (RMSE) of 0.104. Both RF and MLP significantly outperformed the linear SMOreg approach, demonstrating the suitability of modern machine learning techniques. Additionally, the ANFIS model achieved an exceptional R-squared accuracy of 0.99. The findings underscore the potential of data-driven models for accurate flood estimating, providing a valuable benchmark for algorithm selection in flood risk management

An intelligent decision support system for groundwater supply management and electromechanical infrastructure controls

This study presents an intelligent Decision Support System (DSS) aimed at bridging the theoretical-practical gap in groundwater management. The ongoing demand for sophisticated systems capable of interpreting extensive data to inform sustainable groundwater decision- making underscores the critical nature of this research. To meet this challenge, telemetry data from six randomly selected wells were used to establish a comprehensive database of groundwater pumping parameters, including flow rate, pressure, and current intensity. Statistical analysis of these parameters led to the determination of threshold values for critical factors such as water pressure and electrical current. Additionally, a soft sensor was developed using a Random Forest (RF) machine learning algorithm, enabling real-time forecasting of key variables. This was achieved by continuously comparing live telemetry data to pump design specifications and results from regular field testing. The proposed machine learning model ensures robust empirical monitoring of well and pump health. Furthermore, expert operational knowledge from water management professionals, gathered through a Classical Delphi (CD) technique, was seamlessly integrated. This collective expertise culminated in a data-driven framework for sustainable groundwater facilities monitoring. In conclusion, this innovative DSS not only addresses the theory-application gap but also leverages the power of data analytics and expert knowledge to provide high-precision online insights, thereby optimizing groundwater management practices