Abstract Municipal solid waste (MSW) landfills need regular monitoring to ensure proper operations and meet environmental protection requirements. One requirement is to monitor landfill gas emissions from the landfill cover while another requirement is to monitor the potential settlement and damage to MSW landfill covers. Current surveying methods on a landfill cover are time- and labor-intensive and have limited spatial coverage. Landfill operators and researchers have developed unmanned aerial vehicle (UAV)-based monitoring over recent years; however, UAV-based automatic detection of water ponding in landfills has not been studied. Hence, this study proposes a UAV-based approach to monitor landfills and detect water ponding issues on covers by using multimodal sensor fusion. Data acquired from sensors mounted on a UAV were combined, leading to the creation of a ponding index (PI). This index was used to detect potential ponding sites or areas of topographical depression. The proposed approach has been applied in a case study of a closed MSW landfill before and after Hurricane Ian. A comparison between the generated PI map and a manual survey revealed a satisfactory performance with an IoU score of 70.74%. Hence, the utilization of UAV-based data fusing and the developed PI offers efficient identification of potential ponding areas.

Abstract This research introduces a novel nonlinear Muskingum model for river flood routing, aiming to enhance accuracy in modeling. It integrates lateral inflows using the Whale Optimization Algorithm (WOA) and employs a distributed Muskingum model, dividing river reaches into smaller intervals for precise calculations. The primary goal is to minimize the Sum of Square Errors (SSE) between the observed and modeled outflows. Our methodology is applied to six distinct flood hydrographs, revealing its versatility and efficacy. For Lawler's and Dinavar's flood data, the single-reach Muskingum model outperforms multi-reach versions, demonstrating its effectiveness in handling lateral inflows. For Lawler's data, the single-reach model (NR = 1) yields optimal parameters of K = 0.392, x = 0.027, m = 1.511, and β = 0.010, delivering superior results. Conversely, when fitting flood data from Wilson, Wye, Linsley, and Viessman and Lewis, the multi-reach Muskingum model exhibits better overall performance. Remarkably, the model excels with the Viessman and Lewis flood data, especially with two reaches (NR = 2), achieving a 21.6% SSE improvement while employing the same parameter set. This research represents a significant advancement in flood modeling, offering heightened accuracy and adaptability in river flood routing.

Abstract A fundamental issue in the hydraulics of movable bed channels is the measurement of friction factor (λ), which represents the head loss because of hydraulic resistance. The execution of experiments in the laboratory hinders the predictability of λ over a short period of time. The major challenges that arise with traditional forecasting approaches are due to their subjective nature and reliance on various assumptions. Therefore, advanced machine learning (ML) and artificial intelligence approaches can be utilized to overcome this tedious task. Here, eight different ML techniques have been employed to predict the λ using eight different input features. To compare the performance of models, various error metrics have been assessed and compared. The graphical inferences from heatmap data visualization, Taylor diagram, sensitivity analysis, and parametric analysis with different input scenarios (ISs) have been carried out. Based on the outcome of the study, it has been observed that K Star in the IS1 with correlation coefficient (R2) value equal to 0.9716 followed by M5 Prime (0.9712) and Random Forest (0.9603) in IS2 and IS4, respectively, have provided better results as compared to the other ML models to predict λ in terms of least errors.

Abstract Leakages in water distribution networks (WDNs) affect the hydraulic state of the entire or a large part of the network. Statistical correlation computed among pressure sensors monitoring network nodes aids the detection and localization of such leaks. This opens the possibility to work with water network databases, where graph signal processing (GSP) tools aid to understand changes in pressure signals due to leakages in the hydraulic system. This paper presents a methodology to time-varying pressure signals on graph structures. The core of this methodology is based on changing of pressure, due to leaks, that modifies the graph structure. Computing for each time step a new topology of the graph and applying centrality analysis based on PageRank, it is possible to identify the presence of new leaks at the water system. A confusion matrix evaluates the precision of the proposed methodology on defining where and when such leakages start and end. Seven leaks are used to validate the process, which presented 86% in accuracy terms. The results show the benefits of the method in terms of speed, computational efficiency, and precision in detecting leakages.

Abstract The analysis of the spatial and temporal distribution of storm events contributes to a better use of water resources, for example, the supply of drinking water, irrigation practices, electricity generation and management of extreme events to control floods and mitigate droughts, among others. The traditional observation of rainfall fields in Mexico has been carried out using rain gauge network data, but their spatial representativeness is unsatisfactory. Therefore, this study reviewed the possibility of obtaining better estimates of the spatial distribution of daily rainfall considering information from three different databases, which include rain gauge measurements and remotely sensed precipitation products of satellite systems and weather radars. In order to determine a two-dimensional rainfall distribution, the information has been merged with a sequential data assimilation scheme up to the diagnostic stage, paying attention to the benefit that the rain gauge network density has on the estimation. With the application of the Barnes method, historical events in the Mexican territory were analyzed using statistical parameters for the validation of the estimates, with satisfactory results because the assimilated rainfalls turned out to be better approximations than the values calculated with the individual databases, even for a not very low density of surface observations.

Abstract This study aims to explore the influence of various geometrical and hydraulic parameters on flow behavior and hydraulic conductivity in a single artificial fracture through a series of laboratory experiments. Laboratory experiments were conducted to examine unconfined groundwater flow through an artificially constructed single fracture. The fracture model consisted of varying aperture sizes (3, 9, and 12 mm) and different surface roughness conditions (fine, medium, and coarse sand coatings). Non-Darcian turbulent flow characteristics were observed at different flow rates, and the gradient of Reynolds number versus average flow velocity increased with aperture size. Flow parameters of the Darcian, Izbash, and Forchheimer models were calculated to characterize the flow behavior. Both the Forchheimer and Izbash models were found suitable for describing the non-Darcian flow characteristics under the prevailing conditions. The study revealed that hydraulic conductivity depended on flow length for fractures with different apertures and surface roughnesses, likely due to the presence of 2-D torturous flow within the rough fracture surface. These findings contribute to a better understanding of groundwater flow in fractured rock aquifers and provide valuable insights for modeling and managing such systems.

Abstract Monitoring of sewer networks (SNs) is an important task whose planning can be related to various purposes, for example contaminant detection and epidemiological studies. This paper proposes two different approaches for the identification of a monitoring system in SNs. The first one proposes the identification of the best monitoring points starting from the knowledge of the hydraulic behavior of the system with respect to specific sensor threshold values through an optimization procedure that maximizes the reliability in detecting a contaminant. A new mathematical model is developed and a global optimization solver is employed to perform the optimization procedure. The second approach is based on the complex network theory (CNT) tools, adopting the in-relevance-based harmonic centrality, and does not require any hydraulic simulation. The metric is evaluated for each node of the network and provides a range of nodes, classified with respect to their importance, useful to identify suitable locations for sensors. With reference to both a benchmark and a real SN, the comparison between the results achieved by both strategies indicates that the two approaches provide comparable solutions in terms of sensor location.

Abstract Preventing plunge pool scouring in hydraulic structures is crucial in hydraulic engineering. Although many studies have been conducted experimentally to determine relationship between the scour depth and water jets in several fields, available equations have deficiencies in calculating the exact scour due to complexity of scour process. This study investigated local scour depth in plunge pool using Metaheuristic Artificial Bee Colony-Optimized Feed Forward Neural Network (ABCFFNN), variational mode decomposition (VMD) and ensemble empirical mode decomposition (EEMD) techniques. To set modeling, the input parameters are impact angle, densimetric Froude number, impingement length, and nozzle diameter. The models' training and testing were conducted using data available in the literature. The models' performances were compared with experiments. The results demonstrate that scour depth, length, width, and ridge height can be calculated more accurately than available equations. A rank analysis was also applied to obtain the most critical parameter in predicting scour parameters in water jet scouring. ABC-FFNN, VMD-ABCFFNN and EEMD-VMD-FFNN hybrid models were performed to obtain scour parameters. As a result, ABC-FFNN algorithms produced the best solution to predict the scour due to circular water jets, with the values for training (R2: 0.331 to 0.778) and testing (R2: 0.495 to 0.863).

Abstract River water level prediction (WLP) plays an important role in flood control, navigation, and water supply. In this study, a WaveNet-based convolutional neural network (WCNN) with a lightweight structure and good parallelism was developed to improve the prediction accuracy and time effectiveness of WLP. It was applied to predict 1/2/3 days ahead the water levels at the Waizhou gauging station of the Ganjiang River (GR) in China, and it was compared with two recurrent neural networks (long short-term memory (LSTM) and gated recurrent unit (GRU)). The results showed that the WCNN model achieved the best prediction performance with the fewest training parameters and time. Compared with the LSTM and GRU models in the 1-day ahead prediction, the training parameters were reduced from 73,851 and 55,851 to 32,937, respectively. The root mean square error (RMSE) was reduced from 0.071 and 0.076 to 0.057, respectively. The mean absolute error (MAE) was reduced from 0.052 and 0.059 to 0.038, respectively. The Nash–Sutcliffe efficiency (NSE) and coefficient of determination (R2) both increased to 0.998. This result indicated that the improved model was more efficient for WLP.

Abstract Sustainable western US municipal water system (MWS) management depends on quantifying the impacts of supply and demand dynamics on system infrastructure reliability and vulnerability. Systems modeling can replicate the interactions but extensive parameterization, high complexity, and long development cycles present barriers to widespread adoption. To address these challenges, we develop the Machine Learning Water Systems Model (ML-WSM) – a novel application of data-driven modeling for MWS management. We apply the ML-WSM framework to the Salt Lake City, Utah water system, where we benchmark prediction performance on the seasonal response of reservoir levels, groundwater withdrawal, and imported water requests to climate anomalies at a daily resolution against an existing systems model. The ML-WSM accurately predicts the seasonal dynamics of all components; especially during supply-limiting conditions (KGE > 0.88, PBias < ±3%). Extreme wet conditions challenged model skill but the ML-WSM communicated the appropriate seasonal trends and relationships to component thresholds (e.g., reservoir dead pool). The model correctly classified nearly all instances of vulnerability (83%) and peak severity (100%), encouraging its use as a guidance tool that complements systems models for evaluating the influences of climate on MWS performance.