ABSTRACT Urban drainage pipes face increasing risk of sediment deposition, which reduces drainage capacity and increases blockage potential. This study experimentally investigated sediment deposition using a laboratory-scale drainage pipe system with a roughened pipe wall and an image-recognition-based monitoring method. 130 experiments were conducted by varying flow velocity, pipe slope, sediment concentration (loading conditions), and sediment type. Under the tested conditions, sand deposition development exhibited critical hydraulic thresholds, with critical pipe slopes of approximately 6 and 3‰ under low and high concentration conditions, respectively, and a critical flow velocity of 0.4–0.5 m/s. Higher sediment loading conditions promoted deposition, particularly at larger pipe slopes and flow velocities. Compared with sand, cohesive sediments produced longer and more spatially distributed deposits, while mixed sediments exhibited interaction effects rather than simple superposition. Sediment deposition was concentrated in the upstream pipe region, with the maximum deposition thickness occurring at approximately 2% of the pipe length, and generally followed a two-stage temporal evolution from rapid accumulation to quasi-equilibrium. These findings provide improved, empirically grounded understanding of sediment deposition processes, including the experimental identification of critical hydraulic thresholds and sediment-type interaction effects, offering practical guidance for drainage pipe design, targeted sediment removal, and proactive blockage management.
ABSTRACT Accurate forecasting of residential water demand is essential for sustainable infrastructure planning in arid regions facing water scarcity. Kuwait, among the world's most water-stressed nations, depends almost entirely on desalinated water for domestic supply, yet spatially explicit, long-horizon demand forecasts remain scarce. This study applies the cellular automata Markov chain model (CA-MCM) to forecast residential water demand across Kuwait for 2050 and 2100. Three population density raster maps at 90-metre resolution (1975, 2000, 2025) were reclassified into five ordinal classes and used as inputs for CA-MCM transition analysis in TerrSet v20 (LiberaGIS). Model accuracy was validated by comparing the predicted 2025 class map to actual data, yielding an area under the curve of 0.980 via receiver operating characteristic (ROC) analysis, and a Kstandard of 0.978 and Klocation of 0.989 through the multi-resolution VALIDATE procedure. Residential water demand was then computed at the pixel level using a per-capita consumption rate of 500 litres per person per day. Results show marked spatial intensification and expansion of high-demand zones, with total forecasted demand reaching approximately 3.25 million m3/day by 2050 and 4.53 million m3/day by 2100. These findings offer spatially explicit guidance for long-term water security investment planning in Kuwait.
ABSTRACT Accurate leak detection is fundamental to ensuring the operation of water distribution networks (WDNs). However, most existing acoustic-based methods are rarely applied in real WDNs because they often capture only incomplete or single-type features, resulting in low leak detection accuracy. This study proposes a multistage, multidimensional spectrogram-based leak detection method using real acoustic signal data, which includes the following features: (1) reconstruction of original acoustic signals using the Iterative Amplitude Adapted Fourier Transform to enhance the size of the original dataset; (2) conversion of acoustic signals into multidimensional spectrograms that integrate Mel, linear, and logarithmic spectrograms as inputs; and (3) adoption of a two-dimensional residual convolutional neural network (Res2D-CNN) model for leak detection. Compared with inputs with nonreconstructed original data and single spectrogram feature, as well as against multiple methods such as SVM, one-dimensional residual CNN (Res1D-CNN), CNN, the hybrid CNN and Informer (CNN-Informer), the hybrid CNN and random forests (CNN-RF), and SqueezeNet, the proposed method achieves superior performance across all evaluation metrics, including accuracy, precision, F1-score, specificity, and sensitivity, with each exceeding 98%. Therefore, this framework is demonstrated to be an effective approach for detecting leaks.
ABSTRACT Boiler water quality directly influences the efficiency, reliability and service life of industrial steam generation systems, and inadequate monitoring can cause scaling, corrosion, reduced heat transfer efficiency and unplanned shutdowns. This study presents an intelligent Internet of Things (IoT)-based framework for continuous boiler water quality monitoring and early warning that integrates real-time sensing, statistical process control (SPC), rule-based decision support and machine learning. Six key parameters are monitored continuously: pH, conductivity, chloride concentration, hardness, dissolved oxygen and temperature. SPC performs real-time process monitoring, while a rule-based decision support system generates corrective recommendations from predefined operational limits. To validate the framework, nine supervised machine learning models were evaluated using representative operating scenarios; Random Forest and LightGBM achieved the highest classification performance, with 99.5% accuracy under 10-fold cross-validation. Correlation analysis showed strong positive relationships between conductivity and hardness (r = 0.73), conductivity and chloride (r = 0.70), and hardness and chloride (r = 0.72). Feature importance analysis identified temperature (31.9%), pH (27.9%) and hardness (18.1%) as the most influential variables. The framework enables continuous monitoring, rapid identification of abnormal boiler conditions and timely operational decision support, demonstrating proof-of-concept capability for further validation with long-term industrial boiler data.
ABSTRACT Graph shows the three steps of the paper, from top to bottom: Construction of the UFR system, Analysis of UFR and Propose some strategies. Global extreme climate change and urbanization have intensified urban flood events, impacting the sustainable development of resilient cities. Urban flood resilience (UFR) is a new approach to flood prevention and risk management. However, previous studies have rarely assessed UFR at the urban agglomeration scale using long-term time series data, and have overlooked the dynamic evolution characteristics. This study analysed five urban agglomerations in Henan that are susceptible to flooding. First, a UFR indicator system was developed. The UFR level was then calculated using the improved CRITIC method. The evolution characteristics and constraint factors of UFR were analysed using the kernel density method and the barrier degree model. The results show that (1) UFR levels of Henan are generally low (0.256–0.602), with the highest level in central Henan, followed by west Henan, south Henan, north Henan, and east Henan. (2) Infrastructure resilience makes the largest contribution. Economic resilience and social resilience show a downward trend in most cities. (3) The UFR has polarization characteristics in Henan. (4) The major factors hindering UFR are surface water resources and flood prevention construction funds. Finally, some strategies were proposed to improve UFR. This study contributes to enhancing flood prevention capacity and developing urban resilience.
