Rule-based BPNN model for real-time IDF prediction of rainfall: Valuable Input for Early Warning Systems

Rainfall data sources constitute a vital component of flood early warning systems (EWS), and their inseparability from these systems is evident [1]. However, the information derived from these sources is typically confined to the duration, intensity and peak time for ground-based stations and cloud density and temperature for satellite productions [2]. Therefore, more details into the current rainfall occurrence and predictions regarding its future characteristics can significantly assist real-time flood forecasting systems to perform more accurate and reliable measures [3]. One of the rainfall characteristics that can bring valuable insight into the EWS are return period (RP) or position of rainfall into the intensity-duration-frequency (IDF) curves. This new parameter can offer a more nuanced understanding of rainfall events and significantly enhance the capabilities of early warning systems [4]. In this study, a novel Back Propagation Neural Network model is designed to enhance the accuracy of rainfall predictions in EWS. The model incorporates five rainfall inputs of (1) current Intensity, (2) intensity gradient determined from an intensity library, (3) current duration, (4) current RP determined using rules from the IDF curve library, (5) RP gradient, (6) absolute energy, and (7) anthropic class. The model employs two 5-neuron hidden layers to predict the RP class of current rainfall, i.e. a 5-year or 3-month RP for instance, depending on the desired lead time. To evaluate its accuracy, the model is tested for various time predictions with 15-minute intervals. Subsequently, a real case study of an urban drainage system in the UK is chosen to assess how this additional input enhances previously developed models [3-4]. The results demonstrate that the model excels in predicting the RP for a 2-hour lead time, achieving a performance accuracy exceeding 90%. Moreover, an acceptable accuracy rate of over 75% is achieved for a 4-hour lead time. Additionally, the incorporation of an added parameter into a benchmark EWS results in a 10.8% increase in accuracy for 15-min, escalating to 37.8% for 4-hour lead time. Although the influence of the added parameter may be minimal for near timesteps, its impact becomes significantly more pronounced when dealing with longer lead time predictions, exactly when conventional EWS performance tends to be reduced.Peer reviewe

Time-series Boosting in Ensemble Modelling of Real-Time Flood Forecasting Application

© Author(s) 2023. This work is distributed under the Creative Commons Attribution 4.0 License.While concept of boosting ensemble data mining techniques has been recently attracted a lot of attention for flood forecasting, mainly on non-urbanised river basins or reservoirs [1,2], time-series boosting, i.e., contribution of last timestep prediction to the next forecasting model is a new era, especially for real-time operation of flood forecasting models in the shape of early warning systems. This study aims to provide time-series boosting for ensemble flood forecasting model through adding forecasted water level of one timestep before as an input of training base models to previous proposed rainfall feature, especially rainfall duration, intensity, evidence of past rainfall and season occurrence [3]. Several weak learner data mining techniques are developed for various forecast lead times and recorded in data cube structure that can be used for developing time-series boosted ensemble model. This novel model was tested for real case study of Hanwell urban drainage systems located in the west London, UK for a period of 20 years data with 15min intervals. Confusion matrix is employed for performance assessment and the model is compared by conventional benchmark gradient boosted models. Results shows the added feature can significantly increase the accuracy of overflow detection of all developed base models, especially for longer timesteps. More specifically, adding the new feature to the model can increase the accuracy rate from 84% for the best developed base model to 93% in 3hrs-ahead predictions. More importantly, the model can decrease underestimation miss rate from 45% to only 21% for the same forecast lead time. Furthermore, new time-series boosted ensemble model can noticeably increase overflow detection rate, where hit rate increase from 78% to 88% in 3hrs-ahead predictions. Overall, the concept of time-series boosted ensemble modelling can overcome the problem of missing and false alarm of real-time operation by adding the previous situation of catchment to the forecasting procedure.Peer reviewe

Physics-Informed AI-based Modelling for Flood Early Warning Systems

Today, the vast majority of early warning systems (EWS) are introduced in which advanced deep learning, recurrent neural network or ensemble-based data mining techniques are applied to provide more accurate and reliable flood forecasting [1]. This trend have been gained more trends mainly due to recent advances in computational capabilities, technological enhancement, and data science-based modelling have empowered these data-driven models [2]. A novel addition in this community is the physics-informed neural network models (PINN), integrating physical principles and constraints into architecture of data driven models. This hybrid approach is particularly beneficial in scenarios where prior knowledge of underlying physics such as nature of rainfall occurrence or catchments hydraulic characteristics are limited [3]. In the present study, PINN-based ensemble multi-class data mining model, inspired by [4] is introduced for forecasting water level classes ranging from no risk to high risk in the context of urban drainage systems (UDS). To keep simplicity, this model is developed with only two datasets: rainfall and UDS water levels. In addition to conventional inputs such as rainfall intensity, duration, session, and soil moisture, two physics-informed rainfall inputs - namely, the potential future return period (RP) of current rainfall and the current return period class - are incorporated. Additionally, two physics-informed catchment water level inputs - specifically, the water level class at the current timestep and the duration of the current class - are integrated into the model framework. The introduction of these new parameters aims to offer valuable insights into system dynamics, enhancing the model's ability to comprehend both short-term and long-term memory patterns. The results, assessed using the method outlined in [2], indicate a substantial improvement in hit rates - from 67% to 88% - compared to a benchmark model. Notably, time lags in the correct detection of water level classes, are halved on average, reducing from 2-timstep intervals. More specifically, the rate of event underestimation decreases from 7% to 2%, showcasing that the new method has the potential to reduce false alarms in EWS. It is essential to note that the application of PINN is currently limited to using only physics-informed input data. However, a promising avenue for future exploration involves extending this approach to adjusting hyperparameters of data-driven models with physics equations. This adaptation is recommended for future directions in research and application.Peer reviewe

Enhancing Urban Flood Prediction Accuracy with Physics-Informed Neural Networks: A Case Study in Real-Time Rainfall Data Integration

Urban flooding presents significant socio-economic challenges in cities, emphasising the need for effective flood forecasting [1]. Traditional flood prediction methods are data-intensive and time-consuming for calibration and computation. However, due to data scarcity and the necessity to account for real-time variable factors, Machine/Deep Learning (ML/DL) techniques are emerging as preferred solutions. These methods offer an advantage over slow, yet accurate, calibrated numerical models by handling limitations more efficiently [2]. More recently, a notable DL technique, called the Physics-Informed Neural Network (PINN), integrates physics understanding into the modeling process. This approach enables the model to incorporate physical principles into its inputs, enhancing its predictive capabilities despite limited data availability. Similar to other DL models, PINNs consist of an input layer, several hidden layers, and an output layer. However, as added value, the structure of these layers in PINN models varies based on the problem's nature and hyperparameters such as weights and biases are adjusted based on physical equations/roles/formula during the training phase to minimise the loss function [3]. Application of PINN models have been tasted widely in other contexts such as groundwater systems, climate prediction, energy systems, and waste management [4]. However, in the context of real-time flood early warning systems, this issue remains relatively novel. This study aims to develop a PINN model to detect flood events at specific points in an urban drainage system at the earlier timesteps of rainfall. The model employs the Horton equation applied to the rainfall hyetograph (both time-dependent) to process real-time data. This input allows the model to predict water level rises at certain points in the channel, identifying potential flooding. This new data is used as both input data and roles of bias adjusting during training model. The results show that by integrating physics-based rainfall inputs, accuracy of prediction have been significantly enhanced especially for longer timesteps in comparison to well-developed ML models.Peer reviewe

Application of Innovative Digital Technologies in Urban Flood Risk Management

© Author(s) 2023. This work is distributed under the Creative Commons Attribution 4.0 License.Climate change can lead to several devastating hazards, including extreme rainfall and alteration of precipitation patterns that both contribute to more urban floods and various repercussions on urban life and infrastructure [1]. The establishment of risk management strategies along with engaging involved parties, i.e., authorities and publics, has become an integral part of mitigating strategies for growing urban flood risk [2]. These control measures have undergone several principal transformations in recent years particularly due to development of the real-time early warning of flood forecasting systems associated with digital innovative technologies such as virtual reality (VR), augmented reality (AR), and digital twin (DT). These technologies have been widely used for not only virtually real-time representation of formation and development of urban flooding but also raising stakeholder knowledge and awareness regarding the consequences of flood risk [3,4]. In this research work, the application of digital innovative technologies in the digital visualisation of urban floods and increasing stakeholder awareness has been investigated. To begin with, VR has been widely used to model pluvial floods by creating a simulated artificial 3D environment that allows users to explore and interact with virtual surroundings. AR has been implemented through the development of mobile apps that enables the user to investigate the possibility of a flood. DT commencing an efficient flood risk communication tool to provide the user with information about the current condition, potential risks, and flood-prone areas that are integrated into the complex real-time digital system made up of numerous sensors, logic devices, and predictive functions in urban areas. The results of investigation show while conventional technologies have often concentrated on authorities, the above innovative technologies have shifted their focus to local authorities and public. VR has been comprehensively employed to engage them in risk control management through allowing the users to interact with the system under risks. AR is mainly utilised to serve the public through installed software on their phones and investigating flood-prone areas. The focus of DT has been on involving authorities and operators to understand the real-time information about flood hydraulics and function of urban system and components. Despite the extensive capabilities, DT has yet to be properly taken into account and, if properly presented, can be effective in raising public awareness especially because of its significant abilities in the virtual representation of interactions within the system.Peer reviewe

Unveiling the Interplay: Flood Impacts on Transportation, Vulnerable Communities, and Early Warning Systems

© 2024 The Author(s). This is an open access work distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/Flooding's impact on transportation infrastructure is crucial, influencing urban mobility, economic activities, and societal resilience [1]. Disruptions in transportation networks during flood events significantly impede access to essential services, intensifying the vulnerability of communities and hindering recovery efforts. Understanding the multifaceted consequences of flooding on transportation is fundamental for fortifying these critical systems against the escalating risks posed by changing climate patterns and extreme weather events [2]. Floods, stemming from various sources like heavy rainfall, storm surges, or river overflow, profoundly affect transportation infrastructure. Bridges, roads, and rail networks face damage or complete destruction, impeding travel and access to crucial services. Moreover, inundated areas and compromised roadways exacerbate accessibility challenges for specific demographic groups [3]. Vulnerable communities, including low-income populations or geographically isolated areas, bear a disproportionate burden, experiencing limited access to jobs, healthcare, and emergency services during and after flood events. Research exploring the nexus between early warning systems and transportation resilience remains sparse but holds significant promise. Early warnings tailored to transportation vulnerabilities could mitigate disruptions, enhancing evacuation plans and rerouting strategies. Enabling timely and targeted information dissemination to affected areas or populations, especially those with limited mobility or access, can substantially reduce the adverse impacts on their daily lives and crucial infrastructure. Understanding the gaps in the interconnection of early warning systems and transportation resilience is crucial for bolstering the adaptive capacity of transportation networks, ensuring equitable access, and minimizing the disproportionate impacts of floods on vulnerable communities.Peer reviewe

Integrated Data-Driven Approach for Early Pollution Detection and Management in the Thames River Ecosystem

The increasing pollution levels in rivers have become a serious concern worldwide due to their detrimental impact on ecosystems and human health. Recently, there has been a growing recognition of the need for early warning systems (EWS) to monitor and manage water quality in river ecosystems [1]. EWS is a method that is used to detect and predict potential risks or hazards before they occur. It helps alert individuals, organisations, or communities and provides them with timely information to take necessary precautions and actions to minimise the impact of the anticipated event [2]. EWS for water quality management also can be efficient when real-time data (both water quality and quantity) can be combined with real-time flood forecasting [3]. This study presents a new method based on data-driven models for early warning pollution detection in the Thames River. The proposed method collects and analyses various types of data, including weather data and water quality parameters obtained from water samples and sensing systems. These inputs are integrated into a robust computational framework to forecast and identify potential pollution incidents in the Thames River system. The data-driven model incorporates real-time weather data to encompass the dynamic nature of pollution levels. The model can identify high-risk situations and issue timely warnings to prevent further pollution by analysing historical weather patterns and their correlation with pollution incidents. The system's computational framework utilises a deep neural network to analyse and interpret the collected data. The model is fine-tuned and calibrated using historic data, allowing it to effectively recognise and predict pollution events in real-time for every flood event through combined sewer overflow structures. By integrating historical and real-time data, the model can enhance predictive capabilities of pollution spread in the river system and hence prepare the relevant bodies to take appropriate actions in time. The proposed method holds great promise in mitigating the adverse impacts of pollution on the river's ecosystem and the surrounding communities. By integrating diverse data sources, including in-situ measurements, sensing systems, and weather information, the model provides a holistic understanding of pollution dynamics and enables proactive pollution control measures. Implementing this model can contribute significantly to preserving the health and ecological integrity of the Thames River, serving as a blueprint for other river systems facing similar pollution challenges worldwide.Peer reviewe

A novel framework for planning policy and responsible stakeholders in industrial wastewater reuse projects: a case study in Iran

Industrial wastewater recycling projects are mainly used for alleviation of both water scarcity and contamination of freshwater bodies. These projects mainly address major challenges related to technological, and economic aspects rather than stakeholders responsibility. Hence, little is known for the role of responsible stakeholders as a major part of planning policy, which requires recognition of their crucial role and integration into associated procedures. This paper presents a new decision support framework to identify responsible stakeholders and reveal the role of their motivations. The approach integrates qualitative and frequency analysis methods into a comprehensive framework to identify the problems over the project lifetime from visible to their roots and link them together with stakeholders through deep mapping. The planning policy framework is applied to a real-world case study of industrial parks in Iran. The results of the case study show that visible economic, social, and technological problems are caused by responsible stakeholders with no direct role in those projects. Additionally, deep mapping analysis shows various deep roots caused by the government and industry are linked to visible problems across all project phases that are related to the role of stakeholders, their behaviour, and deep beliefs

A comprehensive framework for risk probability assessment of landfill fire incidents using fuzzy fault tree analysis

Landfill fire is the most frequent type of incidents in the waste management complexes. This paper presents a new framework for risk probability evaluation of major fires in landfills using the fuzzy fault tree analysis. The framework starts with construction of the fault tree of landfill fire comprised of 38 basic and 22 intermediate events with the corresponding type of faults under managerial, executive, human, and environmental conditions. Fault tree quantitative analysis is carried out through a combination of fuzzy set theory and experts' judgements to overcome the lack of data limitation. Two new sensitivity analysis approaches are used to identify the critical fault type and critical paths in the fault tree. The proposed framework is demonstrated by its application to a real-world case of a landfill in Iran. The results show the probability of a major "fire incident" is 5.5% in which "fire occurrence" stands for 25% higher than "lack of preparation for controlling fire". In addition, "Waste’s uncontrolled dumping" is recognised as the highest critical event by 6% for probability failure and 24% for importance degree. "Executive fault" also found as the most fault’s critical type by frequency analysis of failure probability. The results also reveal the major impact of the experts’ weights, especially for events related to human or management faults. These results can give decision-makers a profound insight into providing effective intervention strategies for minimising the risk of major landfill fire incidents

Appliance for Water consumption management in showering by using Arduino kit, Energy NExus and SensorS : AWARENESS kit

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