## 4 Discussion  
### Rainwater Ingress Detection Device for Enhanced Wastewater Management  
Accurate detection and quantification of rainwater ingress in sewer channels are crucial for effective wastewater management. The novel rainwater ingress detection device utilizes advanced sensor technology to measure parameters such as water level, flow rate, and water quality in real-time [34]. By analyzing the data, the device accurately detects rainwater ingress events and distinguishes them from normal wastewater flow using sophisticated algorithms. It also quantifies the volume and frequency of rainwater infiltration, providing valuable information for assessing the sewer system's capacity and making informed decisions regarding maintenance, upgrades, and resource allocation. The trend indicates that as the pipe diameter increases, the stress level decreases. This inverse relationship is particularly evident when comparing the stress levels at 160 mm and 400 mm diameters, where the stress decreases from 1.49 MPa (Figure 5) to 0.41 MPa (Figure 9). The monitoring of wet circuits in closed channels and examining the connections between  
Figure 10: Spearman correlation for parameters used in PC/ABS. SF—sewage flow; PI—pipe slope inclination; TF—time of water/sewage flow. Figure 9: Visualizing pressure distribution (MPa) in armlet with a wet circuit measurement system for a 400 mm diameter pipe. key parameters, including sewage flow (SF), pipe slope (PI), and time of water/sewage flow (TF), holds significant importance (Figure 10). These factors are pivotal for efficient water and sewage system management and design. Sewage flow (SF) represents the sewage or wastewater flow rate, which is crucial for sizing pipes to handle the generated sewage volume. Pipe slope (PI), indicating pipe inclination, impacts flow velocity and efficiency, with steeper slopes allowing faster flow but potentially requiring more energy for pumping. Time of water/sewage flow (TF) refers to flow duration, which is vital for resource management and insights into system operation. Interpreting the results provides guidance for system adjustments, design choices, and resource allocation in innovations for urban wastewater management and advancing sewage transformation strategies. The other results demonstrated bi-LSTM's (bidirectional Long Short-Term Memory) effectiveness in predicting time series, particularly in forecasting wastewater flow for a treatment plant. The approach successfully achieved sequence-to-point p-step prediction by using past information and a sliding time window. This highlights the potential of deep neural networks for accurate forecasting in practical applications like wastewater management. Further research should explore scalability and compare bi-LSTM with other methods for enhanced efficiency in diverse fields [35]. The hydraulic performance of all tested measurement straps was generally positive. The tests were conducted at flow rates of 3600 and 7200 \(\text{dm}^{3}/\text{h}\). A short trial of up to 0.5 h and a long trial of 5 h were carried out separately for each flow rate. Notably, none of the tested measurement straps exhibited any displacement. Rainwater ingress in sewer channels poses significant challenges for urban wastewater management systems, leading to capacity issues, increased treatment costs, and potential environmental pollution. This paper presents the development of a novel device aimed at enhancing rainwater ingress detection in sewer channels. The device provides real-time monitoring capabilities and enables proactive maintenance strategies, thereby improving the overall performance and resilience of urban wastewater management systems [36]. This paper delves into the meticulous design, implementation, and assessment of a ground-breaking device with the utmost potential to optimize resource allocation and effectively counteract the detrimental effects of rainwater infiltration.