Multivariate geo-sensory time series prediction is challenging because of the complex spatial and temporal correlation. In urban water distribution systems (WDS), numerous spatial-correlated sensors have been deployed to continuously collect hydraulic data. Forecasts of monitored flow and pressure time series are of vital importance for operational decision making, alerts and anomaly detection. To address this issue, we proposed a hybrid dual-stage spatial-temporal attention-based recurrent neural networks (hDS-RNN). Our model consists of two stages: a spatial attention-based encoder and a temporal attention-based decoder. Specifically, a hybrid spatial attention mechanism that employs inputs along temporal and spatial axes is proposed. Experiments on a real-world dataset are conducted and demonstrate that our model outperformed 9 baseline models in flow and pressure series prediction in WDS.
University of Patras（帕特雷大学）；Roma Tre University（罗马第三大学）；National Technical University of Athens（雅典国立技术大学）
Machine and statistical learning algorithms can be reliably automated and applied at scale. Therefore, they can constitute a considerable asset for designing practical forecasting systems, such as those related to urban water demand. Quantile regression algorithms are statistical and machine learning algorithms that can provide probabilistic forecasts in a straightforward way, and have not been applied so far for urban water demand forecasting. In this work, we aim to fill this gap by automating and extensively comparing several quantile-regression-based practical systems for probabilistic one…
VanBerlo Consulting（范伯洛咨询公司）；The Corporation of the City of London（伦敦市公司）
This study describes an investigation into the modelling of citywide water consumption in London, Canada. Multiple modelling techniques were evaluated for the task of univariate time series forecasting with water consumption, including linear regression, Facebook's Prophet method, recurrent neural networks, and convolutional neural networks. Prophet was identified as the model of choice, having achieved a mean absolute percentage error of 2.51%, averaged across a 5-fold cross validation. Prophet was also found to have other advantages deemed valuable to water demand management stakeholders, in…
Northeastern University（东北大学）；Abiomed（艾比奥医疗公司）；UC San Diego（加利福尼亚大学圣迭戈分校）
Mean aortic pressure (MAP) is a major determinant of perfusion in all organs systems. The ability to forecast MAP would enhance the ability of physicians to estimate prognosis of the patient and assist in early detection of hemodynamic instability. However, forecasting MAP is challenging because the blood pressure (BP) time series is noisy and can be highly non-stationary. The aim of this study was to forecast the mean aortic pressure five minutes in advance, using the 25 Hz time series data of previous five minutes as input. We provide a benchmark study of different deep learning models for B…
Urban Water Consumption Forecasting Using Deep Learning and Correlated District Metered Areas
KIOS Research and Innovation Center of Excellence（KIOS研究与创新中心）；Department of Electrical and Computer Engineering（电气与计算机工程系）；University of Cyprus（塞浦路斯大学）
Water demand is a highly important variable for operational control and decision making. Hence, the development of accurate forecasts is a valuable field of research to further improve the efficiency of water utilities. Focusing on probabilistic multi-step-ahead forecasting, a time series model is introduced, to capture typical autoregressive, calendar and seasonal effects, to account for time-varying variance, and to quantify the uncertainty and path-dependency of the water demand process. To deal with the high complexity of the water demand process a high-dimensional feature space is applied…
Implementing a new fully stepwise decomposition-based sampling technique for the hybrid water level forecasting model in real-world application
School of Cyber Science and Technology, University of Science and Technology of China（中国科学技术大学网络空间安全学院）；School of Internet, Anhui University（安徽大学互联网学院）；Chinese Academy of Sciences, Institute of Plasma Physics（中国科学院等离子体物理研究所）；School of Software Technology, Zhejiang University（浙江大学软件学院）
Quaid-i-Azam University（真纳大学）；University of Bradford（布拉德福德大学）；Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust（布拉德福德教学医院NHS基金会信托布拉德福德健康研究所）；COMSATS Institute of Information Technology（COMSATS信息技术学院）；King Saud University（沙特国王大学）；Workers University（工人大学）
These days human beings are facing many environmental challenges due to frequently occurring drought hazards. It may have an effect on the countrys environment, the community, and industries. Several adverse impacts of drought hazard are continued in Pakistan, including other hazards. However, early measurement and detection of drought can provide guidance to water resources management for employing drought mitigation policies. In this paper, we used a multilayer perceptron neural network (MLPNN) algorithm for drought forecasting. We applied and tested MLPNN algorithm on monthly time series da…
Predicting flood for any location at times of extreme storms is a longstanding problem that has utmost importance in emergency management. Conventional methods that aim to predict water levels in streams use advanced hydrological models still lack of giving accurate forecasts everywhere. This study aims to explore artificial deep neural networks' performance on flood prediction. While providing models that can be used in forecasting stream stage, this paper presents a dataset that focuses on the connectivity of data points on river networks. It also shows that neural networks can be very helpf…
A Novel Deep Neural Network Architecture for Real-Time Water Demand Forecasting
School of Automation and Electrical Engineering, University of Science and Technology Beijing（自动化与电气工程学院，科学技术大学）；Institute of Artificial Intelligence, University of Science and Technology Beijing（人工智能研究院，科学技术大学）；Bogazici University, Istanbul Turkey（博雅大学，伊斯坦布尔土耳其）
The contribution of this study is a novel approach to introduce mean reversion in multi-step-ahead forecasts of state-space models. This approach is demonstrated in a prawn pond water quality forecasting application. The mean reversion constrains forecasts by gradually drawing them to an average of previously observed dynamics. This corrects deviations in forecasts caused by irregularities such as chaotic, non-linear, and stochastic trends. The key features of the approach include (1) it enforces mean reversion, (2) it provides a means to model both short and long-term dynamics, (3) it is able…
We present a decision support system for managing water quality in prawn ponds. The system uses various sources of data and deep learning models in a novel way to provide 24-hour forecasting and anomaly detection of water quality parameters. It provides prawn farmers with tools to proactively avoid a poor growing environment, thereby optimising growth and reducing the risk of losing stock. This is a major shift for farmers who are forced to manage ponds by reactively correcting poor water quality conditions. To our knowledge, we are the first to apply Transformer as an anomaly detection model,…
Generative Spatio-temporal GraphNet for Transonic Wing Pressure Distribution Forecasting
Faculty of Engineering and Physical Sciences, University of Southampton（南安普顿大学工程与物理科学学院）；School of Engineering, Zurich University of Applied Sciences ZHAW（苏黎世应用科技大学工程学院）；Federal Institute of Technology Zurich ETHZ（苏黎世联邦理工学院）
A Transformer-based Diffusion Probabilistic Model for Heart Rate and Blood Pressure Forecasting in Intensive Care Unit
The University of Arizona（亚利桑那大学）；Zhongshan Hospital, Fudan University（复旦大学附属中山医院）；The Shanghai Institute of Cardiovascular Diseases（上海市心血管病研究所）；Bio5 Institute, The University of Arizona（亚利桑那大学生物5研究所）；College of Nursing, The University of Arizona（亚利桑那大学护理学院）
Diffusion-Based Rollouts as a Stabilization Mechanism for Long-Horizon Environmental Forecasting
University of California Irvine（加州大学尔湾分校）；San Diego State University（圣地亚哥州立大学）；University of Oklahoma（俄克拉荷马大学）；NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography（NSF可信AI天气、气候与海岸海洋研究所）
Innovation ARIMA models application to predict pressure variations in water supply networks with open-loop control. Case study in Noja (Cantabria, Spain)
Lawrence Livermore National Laboratory（劳伦斯利弗莫尔国家实验室）；TotalEnergies E&P Research and Technology（道达尔能源勘探与生产研究技术中心）；University of North Dakota, Energy & Environmental Research Center（北达科他大学能源与环境研究中心）
Fast forecasting of reservoir pressure distribution in geologic carbon storage (GCS) by assimilating monitoring data is a challenging problem. Due to high drilling cost, GCS projects usually have spatially sparse measurements from wells, leading to high uncertainties in reservoir pressure prediction. To address this challenge, we propose to use low-cost Interferometric Synthetic-Aperture Radar (InSAR) data as monitoring data to infer reservoir pressure build up. We develop a deep learning-accelerated workflow to assimilate surface displacement maps interpreted from InSAR and to forecast dynami…
A Transformer variant for multi-step forecasting of water level and hydrometeorological sensitivity analysis based on explainable artificial intelligence technology
School of Internet, Anhui University（安徽大学互联网学院）；National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University（安徽大学农业生态大数据分析与应用国家工程研究中心）；Forschungszentrum Jülich GmbH, Institute of Energy and Climate Research (IEK)（于利希研究中心能源与气候研究所）；Hefei Institutes of Physical Science, Chinese Academy of Sciences（中国科学院合肥物质科学研究院）
Mohammed V University in Rabat（拉巴特穆罕默德五世大学）；ENSIAS（国家高等信息与系统分析学院（拉巴特穆罕默德五世大学下属，按独立机构提取为ENSIAS，中文暂译：国家高等信息与系统分析学院））；Al Akhawayn University in Ifrane（伊夫兰阿卡韦恩大学）；School of Science and Engineering（科学与工程学院）
Water consumption remains a major concern among the world's future challenges. For applications like load monitoring and demand response, deep learning models are trained using enormous volumes of consumption data in smart cities. On the one hand, the information used is private. For instance, the precise information gathered by a smart meter that is a part of the system's IoT architecture at a consumer's residence may give details about the appliances and, consequently, the consumer's behavior at home. On the other hand, enormous data volumes with sufficient variation are needed for the deep …
Cross-household Transfer Learning Approach with LSTM-based Demand Forecasting
Department of Mathematics and Computer Science（数学与计算机科学系）；Karlstad University, Sweden（卡尔斯塔德大学，瑞典）；Department of Research and Development（研究与发展系）；Thermia AB, Sweden（Thermia AB，瑞典）；American University of Bahrain, Bahrain（巴林美国大学，巴林）
Khulna University of Engineering & Technology（库尔纳工程技术大学）
A ventilator simulation system can make mechanical ventilation easier and more effective. As a result, predicting a patient's ventilator pressure is essential when designing a simulation ventilator. We suggested a hybrid deep learning-based approach to forecast required ventilator pressure for patients. This system is made up of Bi-LSTM and Bi-GRU networks. The SELU activation function was used in our proposed model. MAE and MSE were used to examine the accuracy of the proposed model so that our proposed methodology can be applied to real-world problems. The model performed well against test d…
Department of Hydrodynamic Systems（水动力系统系）；Department of Telecommunications and Media Informatics（电信与媒体信息学系）
Knowing the pressure at all times in each node of a water distribution system (WDS) facilitates safe and efficient operation. Yet, complete measurement data cannot be collected due to the limited number of instruments in a real-life WDS. The data-driven methodology of reconstructing all the nodal pressures by observing only a limited number of nodes is presented in the paper. The reconstruction method is based on K-localized spectral graph filters, wherewith graph convolution on water networks is possible. The effect of the number of layers, layer depth and the degree of the Chebyshev-polynomi…
University of Cambridge（剑桥大学）；German Aerospace Center (DLR)（德国航空航天中心（DLR））
The 100 MW cryogenic liquid oxygen/hydrogen multi-injector combustor BKD operated by the DLR Institute of Space Propulsion is a research platform that allows the study of thermoacoustic instabilities under realistic conditions, representative of small upper stage rocket engines. We use data from BKD experimental campaigns in which the static chamber pressure and fuel-oxidizer ratio are varied such that the first tangential mode of the combustor is excited under some conditions. We train an autoregressive Bayesian neural network model to forecast the amplitude of the dynamic pressure time serie…
