Managing the quality of water for present and future generations of coastal regions should be a central concern of both citizens and public officials. Remote sensing can contribute to the management and monitoring of coastal water and pollutants. Algal blooms are a coastal pollutant that is a cause of concern. Many satellite data, such as MODIS, have been used to generate water-quality products to detect the blooms such as chlorophyll a (Chl-a), a photosynthesis index called fluorescence line height (FLH), and sea surface temperature (SST). It is important to characterize the spatial and temporal variations of these water quality products by using the mathematical models of these products. However, for monitoring, pollution control boards will need nowcasts and forecasts of any pollution. Therefore, we aim to predict the future values of the MODIS Chl-a, FLH, and SST of the water. This will not be limited to one type of water but, rather, will cover different types of water varying in depth and turbidity. This is very significant because the temporal trend of Chl-a, FLH, and SST is dependent on the geospatial and water properties. For this purpose, we will decompose the time series of each pixel into several components: trend, intra-annual variations, seasonal cycle, and stochastic stationary. We explore three such time series machine learning models that can characterize the non-stationary time series data and predict future values, including the Seasonal ARIMA (Auto Regressive Integrated Moving Average) (SARIMA), regression, and neural network. The results indicate that all these methods are effective at modelling Chl-a, FLH, and SST time series and predicting the values reasonably well. However, regression and neural network are found to be the best at predicting Chl-a in all types of water (turbid and shallow). Meanwhile, the SARIMA model provides the best prediction of FLH and SST.
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…
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 tem…
Intraday spatiotemporal PV power prediction at national scale using satellite-based solar forecast models
School of Engineering and Computer Science, Bern University of Applied Sciences（工程与计算机科学学院，伯恩应用科学大学）；Department of Geosciences and Remote Sensing, TU Delft（地球科学与遥感系，代尔夫特理工大学）
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…
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…
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…
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）
Delft University of Technology（代尔夫特理工大学）；VITO（弗拉芒技术研究院）
Due to the increasing integration of solar power into the electrical grid, forecasting short-term solar irradiance has become key for many applications, e.g.~operational planning, power purchases, reserve activation, etc. In this context, as solar generators are geographically dispersed and ground measurements are not always easy to obtain, it is very important to have general models that can predict solar irradiance without the need of local data. In this paper, a model that can perform short-term forecasting of solar irradiance in any general location without the need of ground measurements …
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…
The forecasting and reconstruction of ocean and atmosphere dynamics from satellite observation time series are key challenges. While model-driven representations remain the classic approaches, data-driven representations become more and more appealing to benefit from available large-scale observation and simulation datasets. In this work we investigate the relevance of recently introduced bilinear residual neural network representations, which mimic numerical integration schemes such as Runge-Kutta, for the forecasting and assimilation of geophysical fields from satellite-derived remote sensin…
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）
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）
Can Machine Learning Forecast Rice Yields in Data-Constrained Settings? Satellite Climate Data, National Crop Statistics, and Lessons from Sierra Leone
Seidenberg School of Computer Science & Information Systems Pace University, New York, USA（佩斯大学塞登伯格计算机科学与信息系统学院，纽约，美国）；RiseAfrica Foundation for STEM and Innovation Sierra Leone, West Africa（RiseAfrica STEM与创新基金会，塞拉利昂，西非）
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,…
Institute of Earthquake Forecasting, China Earthquake Administration（中国地震局地震预测研究所）；National Institute of Natural Hazards, Ministry of Emergency Management of China（中国应急管理部国家自然灾害防治研究院）；University of Leicester（莱斯特大学）；Nanyang Technological University（南洋理工大学）；University of Trento（特伦托大学）；National Institute for Nuclear Physics, the Trento Institute for Fundamental Physics and Applications（意大利国家核物理研究院特伦托基础物理与应用研究所）；Chapman University（查普曼大学）；Queen's University Belfast（贝尔法斯特女王大学）；Nantong University（南通大学）
Amongst the available technologies for earthquake research, remote sensing has been commonly used due to its unique features such as fast imaging and wide image-acquisition range. Nevertheless, early studies on pre-earthquake and remote-sensing anomalies are mostly oriented towards anomaly identification and analysis of a single physical parameter. Many analyses are based on singular events, which provide a lack of understanding of this complex natural phenomenon because usually, the earthquake signals are hidden in the environmental noise. The universality of such analysis still is not being …
Satellite-based feature extraction and multivariate time-series prediction of biotoxin contamination in shellfish
NOVA School of Science and Technology (NOVA FCT)（新里斯本大学科学与技术学院（NOVA FCT））；IPMA - Instituto Português do Mar e da Atmosfera（葡萄牙海洋与大气研究所（IPMA））；NOVA Laboratory for Computer Science and Informatics (NOVA LINCS)（新里斯本大学计算机科学与信息学实验室（NOVA LINCS））；Center for Mathematics and Applications (NOVA Math)（新里斯本大学数学与应用中心（NOVA Math））；NOVA Research & Development Unit for Mechanical and Industrial Engineering (UNIDEMI)（新里斯本大学机械与工业工程研发单元（UNIDEMI））
When a territory is poorly instrumented, geostationary satellites data can be useful to predict global solar radiation. In this paper, we use geostationary satellites data to generate 2-D time series of solar radiation for the next hour. The results presented in this paper relate to a particular territory, the Corsica Island, but as data used are available for the entire surface of the globe, our method can be easily exploited to another place. Indeed 2-D hourly time series are extracted from the HelioClim-3 surface solar irradiation database treated by the Heliosat-2 model. Each point of the …
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）
1. Analog forecasting has been successful at producing robust forecasts for a variety of ecological and physical processes. Analog forecasting is a mechanism-free nonlinear method that forecasts a system forward in time by examining how past states deemed similar to the current state moved forward. Previous work on analog forecasting has typically been presented in an empirical or heuristic context, as opposed to a formal statistical context. 2. The model presented here extends the model-based analog method of McDermott and Wikle (2016) by placing analog forecasting within a fully hierarchical…
Frontier Development Lab（前沿发展实验室）；University of Valencia（瓦伦西亚大学）；Arizona State University（亚利桑那州立大学）；NASA Ames Research Center（美国国家航空航天局艾姆斯研究中心）；USGS（美国地质调查局）；RSS-Hydro；University of Bristol（布里斯托尔大学）；University of Oxford（牛津大学）
Where are the Earth's streams flowing right now? Inland surface waters expand with floods and contract with droughts, so there is no one map of our streams. Current satellite approaches are limited to monthly observations that map only the widest streams. These are fed by smaller tributaries that make up much of the dendritic surface network but whose flow is unobserved. A complete map of our daily waters can give us an early warning for where droughts are born: the receding tips of the flowing network. Mapping them over years can give us a map of impermanence of our waters, showing where to e…
Single-mission altimetric water level observations of rivers are spatially and temporally limited, and thus they are often unable to quantify the full extent of extreme flood events. Moreover, only missions with a short-repeat orbit, such as Envisat, Jason-2, or SARAL, could provide meaningful time series of water level variations directly. However, long or non-repeat orbit missions such as CryoSat-2 have a very dense spatial resolution under the trade-off of a repeat time insufficient for time series extraction. Combining data from multiple altimeter missions into a multi-mission product allo…
Satellite-based solar irradiation forecasting is useful for short-term intra-day time horizons, outperforming numerical weather predictions up to 3-4 hours ahead. The main techniques for solar satellite forecast are based on sophisticated cloud motion estimates from geostationary satellite images. This work explores the use of satellite information in a simpler way, namely spatial averages that require almost no preprocessing. Adaptive auto-regressive models are used to assess the impact of this information on the forecasting performance. A complete analysis regarding model selection, the sate…
Rainfall is an important variable to be able to monitor and forecast across Africa, due to its impact on agriculture, food security, climate related diseases and public health. Numerical Weather Models (NWM) are an important component of this work, due to their complete spatial coverage, high resolution, and ability to forecast into the future. In this study, we seek to evaluate the spatiotemporal skill of short-term rainfall forecasts of rainfall across Africa. Specifically, the European Centre for Medium-Range Weather Forecasts (ECMWF) and the National Centers for Environmental Prediction-Gl…
Prithvi-Precip: Integrating Satellite Observations into an Atmospheric AI Foundation Model for Precipitation Forecasting
Colorado State University（科罗拉多州立大学）；IBM Research（IBM研究院）；Earth System Science Center, The University of Alabama in Huntsville（阿拉巴马大学亨茨维尔分校地球系统科学中心）；NASA Marshall Space Flight Center（美国国家航空航天局马歇尔太空飞行中心）；NASA Headquarters（美国国家航空航天局总部）；National Center for Computational Sciences / Oak Ridge National Laboratory（国家计算科学中心/橡树岭国家实验室）；Morgan State Univeresity（摩根州立大学）；Global Modeling and Assimilation Office, NASA Goddard Space Flight Center（美国国家航空航天局戈达德太空飞行中心全球建模与同化办公室）
University of Oxford（牛津大学）；Inria（法国国家信息与自动化研究所）；University College London（伦敦大学学院）；University of Warsaw（华沙大学）
Extreme precipitation events, such as violent rainfall and hail storms, routinely ravage economies and livelihoods around the developing world. Climate change further aggravates this issue. Data-driven deep learning approaches could widen the access to accurate multi-day forecasts, to mitigate against such events. However, there is currently no benchmark dataset dedicated to the study of global precipitation forecasts. In this paper, we introduce \textbf{RainBench}, a new multi-modal benchmark dataset for data-driven precipitation forecasting. It includes simulated satellite data, a selection …
Generative Algorithms for Fusion of Physics-Based Wildfire Spread Models with Satellite Data for Initializing Wildfire Forecasts
University of Southern California（南加州大学）；University of Maryland（马里兰大学）；San Jose State University（圣何塞州立大学）；University of Colorado Denver（科罗拉多大学丹佛分校）；Colorado State University（科罗拉多州立大学）；University of Utah（犹他大学）
Long-Short-Term-Memory (LSTM) networks have been used extensively for time series forecasting in recent years due to their ability of learning patterns over different periods of time. In this paper, this ability is applied to learning the pattern of Global Positioning System (GPS)-based Precipitable Water Vapor (PWV) measurements over a period of 4 hours. The trained model was evaluated on more than 1500 hours of recorded data. It achieves a root mean square error (RMSE) of 0.098 mm for a forecasting interval of 5 minutes in the future, and outperforms the naive approach for a lead-time of up …
DAWP: A framework for global observation forecasting via Data Assimilation and Weather Prediction in satellite observation space
Shanghai Jiao Tong University（上海交通大学）；Fudan University（复旦大学）；The Chinese University of Hong Kong Shanghai AI Laboratory（香港中文大学上海人工智能实验室）；Shanghai AI Laboratory（上海人工智能实验室）
Water vapour in the atmosphere is the main source of the atmospheric opacity in the infrared and sub-millimetric regimes and its value plays a critical role in observations done with instruments working at these wavelengths on ground-based telescopes. The scheduling of scientific observational programs with instruments such as the VLT Imager and Spectrometer for mid Infrared (VISIR) at Cerro Paranal and the Large Binocular Telescope Interferometer (LBTI) at Mount Graham would definitely benefit from the ability to forecast the atmospheric water vapour content. In this contribution we present a…
In this paper, we propose a Light Gradient Boosting (LightGBM) to forecast dominant wave periods in oceanic waters. First, we use the data collected from CDIP buoys and apply various data filtering methods. The data filtering methods allow us to obtain a high-quality dataset for training and validation purposes. We then extract various wave-based features like wave heights, periods, skewness, kurtosis, etc., and atmospheric features like humidity, pressure, and air temperature for the buoys. Afterward, we train algorithms that use LightGBM and Extra Trees through a hv-block cross-validation sc…
Diagnostic vs dynamic representation of the inverse barometer effect in a global ocean model and its potential for probabilistic storm surge forecasting
