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In recent years, there has been severe flooding in urban areas as well as coastal and river flooding. Urban flooding is exacerbated by climate change, urbanization, growing population, and the increase of impervious surfaces in urban areas. Stormwater drainage systems that discharge stormwater to a safe location in urban areas are becoming increasingly important. The objective of this study is to analyze and calibrate the flood performance of stormwater drainage systems currently used in the central region of Malatya in a potential flood situation using geographic information systems and the InfoWorks ICM. The model was created using the land use type, buildings, and digital elevation model (DEM), and the analysis was performed by exposing stormwater drainage systems to rainfall events of 5, 10, and 15 min of duration for return periods of 2, 5, and 10 years. The model was then validated using field-observed rainfall and flood data and its performance was evaluated using R2, NSE, RMSE, and MAE metrics. The results showed that the eight stormwater drainage systems currently in operation cannot fully convey stormwater and may pose a risk of loss of life and property in residential areas. In addition, the severity of the flooding was found to increase with an increasing return period.

Leaks cause significant operational problems in water distribution systems (WDSs). The methods for managing leaks are time-consuming and costly. Therefore, the suitability and applicability of water loss management (WLM) methods should be analyzed. In this study, a new comprehensive framework was proposed using the scoring table to evaluate and highlight the reliability of data and to analyze the current application level of leakage management practices in WDSs. The developed framework consists of 60 sub-components determined to cover the WLM practices. A scoring structure was created to analyze these sub-components in measurable criteria. The developed framework was applied to three pilot administrations, and the results were discussed. The data quality (quite good, good, doubtful, poor, and quite poor) is classified according to the application level of the leakage management practices. The data quality of leakage management components and the application levels of practices are at good level in Administrations I and II and at moderate level in Administration III. The weaknesses and strengths in administrations were defined in the scope of leakage management, and the components that need improvement are determined dynamically. This framework will provide more accurate data for sustainable leakage management in the administration and make field applications more systematic.

Objective This study evaluates the potential use of ChatGPT in aiding clinical decision-making for patients with mild traumatic brain injury (TBI) by assessing the quality of responses it generates for clinical care. Methods Seventeen mild TBI case scenarios were selected from PubMed Central, and each case was analyzed by GPT-4 (March 21, 2024, version) between April 11 and April 20, 2024. Responses were evaluated by four emergency medicine specialists, who rated the ease of understanding, scientific adequacy, and satisfaction with each response using a 7-point Likert scale. Evaluators were also asked to identify critical errors, defined as mistakes in clinical care or interpretation that could lead to morbidity or mortality. The readability of GPT-4’s responses was also assessed using the Flesch Reading Ease and Flesch-Kincaid Grade Level tools. Results There was no significant difference in the ease of understanding between responses with and without critical errors ( p  = 0.133). However, responses with critical errors significantly reduced satisfaction and scientific adequacy ( p  < 0.001). GPT-4 responses were significantly more difficult to read than the case descriptions ( p  < 0.001). Conclusion GPT-4 demonstrates potential utility in clinical decision-making for mild TBI management, offering scientifically appropriate and comprehensible responses. However, critical errors and readability issues limit its immediate implementation in emergency settings without oversight by experienced medical professionals.

A novel optimization model was developed using the equilibrium optimization algorithm to define the most appropriate management process according to the current state of urban water components in utilities. The basis of the optimization model is the current status analysis and management system, which consists of 11 main headings and 231 components. This model is applied for three utilities, and the results are presented in comparison with real-time data. Currently, the number of components with 0 or 1 score is 28, 19 and 69, respectively. The current average scores of the components in the utilities were obtained as 2.84, 3.43 and 2.48, respectively. Then, the improvement process of these components is optimized by the equilibrium optimization algorithm. The most appropriate targets were defined as 3.90, 4.15 and 3.71, respectively, with the optimization algorithm by considering the current scores in the utilities. The target scores for water supply, wastewater collection and treatment components are determined as 3.81, 4.05 and 3.84 for utility I; 4.03, 4.18 and 4.22 for utility II; and 3.51, 3.56 and 4.05 for utility III. The proposed model will be a reference for defining the most appropriate target and determining the management process.

A sustainable urban water cycle is critical in terms of effective water network management, efficient use of water resources, protection of the environment and human health, and reuse of treated water. The objective of this study is to develop a novel assessment framework that evaluates the data quality of components and current implementation levels of wastewater management (WWM) practices, calculates performance indicators according to the data quality of the components, and proposes the most appropriate improvement methods according to the current status of the components. This assessment framework consists of five matrices, namely the current situation analysis and management system (CSAS), data matrix (DATA), performance assessment system (PAAS), target definition system (TARGET), and method matrix (TOOL). The current situation is analyzed with a total of 231 components under 11 main headings covering WWM practices. The assessment framework was tested in pilot utilities and the results were discussed. It is observed that the scores of utility I were generally better than those of utilities II and III. The novelty of this assessment framework is to evaluate the current situation of WWM practices with a unique scoring system, to define the weaknesses and strengths in WWM, and to present a roadmap for improvement according to the current situation.

Water losses in water distribution systems reach significant rates depending on the network characteristics. Various methods, which have initial investment and operating costs, have been applied to reduce these losses. Therefore, appropriate and applicable methods should be preferred by considering the network characteristics. The aim of this study is to determine the economic loss level with an optimization algorithm for utilities with different network characteristics, water production, operating costs and institutional capacity. Three pilot utilities with different system characteristics and water loss components were selected as application areas. The non-revenue water rates are currently calculated as 57%, 50% and 37%, respectively. The economic loss levels in the pilot utilities were calculated as 29%, 16% and 23% with the optimization model. Moreover, the most appropriate methods to be applied according to the conditions of the utilities were determined in order to reach these defined economic loss levels. It is thought that the results obtained from this study will be a reference for the development of sustainable water loss management strategies and their implementation in the field.

Failures and breaks occurring in water distribution networks (WDSs) create significant leakage volumes annually. System operating conditions deteriorate due to the increase in the rate of failure and leakage. Therefore, the failure rate and leakage volume should be reduced by applying the most appropriate methods. For this, the most economically suitable level must first be defined in each system or districtmetered area. This study aims to define the most economical leakage level with the optimization algorithm in the district-metered area in water distribution systems. For this, network characteristics, subscriber information and water consumption, water production cost, failure rates, and other data in the isolated measurement area are considered. Ant lion optimization algorithm was used as the optimization algorithm in the study. The definition of the methods to be applied to reach the defined ELL level constitutes a reference for the implementers. Water utilities can continue their loss reduction strategies in the most economically efficient way with the help of this method. In the selected regions of the study area, pressure management application and active leakage method application were economical. Thus, it is possible to create a more effective and efficient leakage management plan in the isolated measurement area. It is thought that the results obtained from the study will serve as a reference for practitioners and technical personnel, especially in terms of determining the appropriate leakage target level for each isolated region.

In water distribution systems, water losses should be defined accurately and systematically. The water balance method is one of the most basic analyzes applied in water loss management. In this study, a new method was proposed to evaluate the data quality of water balance components and application levels of water balance practices by considering a total of 27 components. The developed model was applied in 4 pilot water administrations in Turkey. The weaknesses and strengths in water balance practices were determined by considering scoring in accordance with the dynamic structure of each administration. The quality of basic data measurement components and application levels of water balance practices were found to be at a good level in Administrations II and IV, at a poor level in Administration I, and at an average level in Administration III. Moreover, quality of water balance analysis and performance monitoring practices are at a good level in Administrations II and IV, and at a poor level in Administrations I and II. Thus the components that need improvement in each administration were identified and an improvement process was suggested. It is thought that this model will make a significant contribution to the testing of current application levels of water loss management components for practitioners and decision makers.

Wastewater system failures cause operating conditions to deteriorate. Therefore, risk factors should be identified and rehabilitation priority should be established by considering all factors. Determining rehabilitation priority areas is very important in terms of public health, service quality and operating cost. The aim of this study, which was carried out in Malatya, Turkey, was to determine rehabilitation priority in wastewater systems by integrating the ENTROPY, ELECTRE and TOPSIS methods. Some 26 physical, hydraulic, operating and cost factors were considered. The factor weightings were determined by the ENTROPY method to define the factors’ contributions, based on the field data. Rehabilitation priorities were then determined separately using ELECTRE and TOPSIS, taking the factor weights and field data into consideration. Priority regions in rehabilitation were obtained similar according to both methods. The results obtained will provide a reference for wastewater system management and determination of rehabilitation priorities.

In this study, a novel approach combining fuzzy clustering and Least Squares Support Vector machine (LS-SVM) methods is developed for estimation of failure rate in water distribution networks and for determination of the relationship between failure rate-effective factors. For this aim, failure data observed Malatya water distribution network during 2006–2012 was selected as study area. In first phase, estimation model was developed and tested for the complete data set in estimating the failure rate by LS-SVM method. Then, in order to develop a more sensitive estimation model and to improve the performance of LS-SVM, 9 sub-regions were defined with similar characteristics by using fuzzy clustering method. Then failure rate estimation was carried out for each of the sub-regions using by LS-SVM method. Feed Forward Neural Network (FFNN) and Generalized Regression Neural Network (GRNN) methods were also used for estimation of failure rate and the results were compared with those of LS-SVM. The criteria such as Correlation Coefficient (R), Efficieny (E) and Root Mean Square Error (RMSE) were used to evaluate the performance of models. The results showed that LS-SVM model gives better results in comparison with the FFNN and GRNN models. It was also determined that LSSVM model results for the sub-regions defined by clustering analysis are better and that the clustering analysis increases the estimation model performance in addition to the fact that the estimation results have become better. In conclusion, it can be possible to develop a more sensitive estimation models using fuzzy clustering and LSSVM methods.