A Systematic Review for Transformer-based Long-term Series Forecasting

The emergence of deep learning has yielded noteworthy advancements in time series forecasting (TSF). Transformer architectures, in particular, have witnessed broad utilization and adoption in TSF tasks. Transformers have proven to be the most successful solution to extract the semantic correlations among the elements within a long sequence. Various variants have enabled transformer architecture to effectively handle long-term time series forecasting (LTSF) tasks. In this article, we first present a comprehensive overview of transformer architectures and their subsequent enhancements developed to address various LTSF tasks. Then, we summarize the publicly available LTSF datasets and relevant evaluation metrics. Furthermore, we provide valuable insights into the best practices and techniques for effectively training transformers in the context of time-series analysis. Lastly, we propose potential research directions in this rapidly evolving field

Greenhouse gas observations across the Land-Ocean Aquatic Continuum: Multi-sensor applications for CO2, CH4 and O2 measurements

Carbon dioxide (CO2) and methane (CH4) are major greenhouse gases (GHG) and have been under constant monitoring for decades. Both gases have significantly increased in recent years due to anthropogenic activities. This has huge detrimental repercussions within natural systems including the warming of the planet. Although these GHG are extremely significant, there are also vast areas of study with little to no data in regards to emissions and budgets. These gaps are mainly within the aquatic regions (or the Land Ocean Aquatic Continuum (LOAC)). As a consequence, there can be large discrepancies between budget numbers and in turn, scaling and future predictions. In order to combine oceanographic and limnological methods this thesis presents a novel sensor package and show its application in multiple campaigns across the entire LOAC. The sensor set-up contained the oceanographic sensors HydroC CO2 FT (pCO2), HydroC CH4 FT (CH4), HydroFlash O2 (O2) and a thermosalinograph for temperature and conductivity measuring continuously, all simultaneously. We extensively mapped ocean to inland regions. The results first describe the processes to enable the set-up to be used across the LOAC boundary over 3 seasons. Extensive corrections were needed for the data to be fully appreciated for all salinities specifically in fresh inland waters. The data was then split between CO2 and CH4, where, in inland waters, further analyses were performed. The area of interest was the Danube Delta, which was found to be continuously supersaturated in regards to CH4 and fluctuating between a source and sink for CO2. Extraction of TA was possible, using the sensors continuous data by applying a simple model. In this extraction and the continuous data, large spatial-variability was observed and further analysed allowed for diel cycle extractions, which are usually disregarded in budgets and measurements. In channels, CH4 concentrations and fluxes were found to potentially be underestimated by up to +25% and +20% respectively when not including a full diel cycle. In lakes however, we found the opposite, with an overestimation in concentration and fluxes (+3.3% and +4.2%) when not considering the diel cycle, although this greatly depends on time of the sampling

Developing Multi-Scale Models for Water Quality Management in Drinking Water Distribution Systems

Drinking water supply systems belong to the group of critical infrastructure systems that support the socioeconomic development of our modern societies. In addition, drinking water infrastructure plays a key role in the protection of public health by providing a common access to clean and safe water for all our municipal, industrial, and firefighting purposes. Yet, in the United States, much of our national water infrastructure is now approaching the end of its useful life while investments in its replacement and rehabilitation have been consistently inadequate. Furthermore, the aging water infrastructure has often been operated empirically, and the embracement of modern technologies in infrastructure monitoring and management has been limited. Deterioration of the water infrastructure and poor water quality management practices both have serious impacts on public health due to the increased likelihood of contamination events and waterborne disease outbreaks. Water quality reaching the consumers’ taps is largely dependent on a group of physical, chemical, and biological interactions that take place as the water transports through the pipes of the distribution system and inside premise plumbing. These interactions include the decay of disinfectant residuals, the formation of disinfection by-products (DBPs), the corrosion of pipe materials, and the growth and accumulation of microbial species. In addition, the highly dynamic nature of the system’s hydraulics adds another layer of complexity as they control the fate and transport of the various constituents. On the other hand, the huge scale of water distribution systems contributes dramatically to this deterioration mainly due to the long transport times between treatment and consumption points. Hence, utilities face a considerable challenge to efficiently manage the water quality in their aging distribution systems, and to stay in compliance with all regulatory standards. By integrating on-line monitoring with real-time simulation and control, smart water networks offer a promising paradigm shift to the way utilities manage water quality in their systems. Yet, multiple scientific gaps and engineering challenges still stand in the way towards the successful implementation of such advanced systems. In general, a fundamental understanding of the different physical, chemical, and biological processes that control the water quality is a crucial first step towards developing useful modeling tools. Furthermore, water quality models need to be accurate; to properly simulate the concentrations of the different constituents at the points of consumption, and fast; to allow their implementation in real-time optimization algorithms that sample different operational scenarios in real-time. On-line water quality monitoring tools need be both reliable and inexpensive to enable the ubiquitous surveillance of the system at all times. The main objective of this dissertation is to create advanced computational tools for water quality management in water distribution systems through the development and application of a multi-scale modeling framework. Since the above-mentioned interactions take place at different length and time scales, this work aims at developing computational models that are capable of providing the best description of each of the processes of interest by properly simulating each of its underlying phenomena at its appropriate scale of resolution. Molecular scale modeling using tools of ab-initio quantum chemical calculations and molecular dynamics simulations is employed to provide detailed descriptions of the chemical reactions happening at the atomistic level with the aim of investigating reaction mechanisms and developing novel materials for environmental sensing. Continuum scale reactive-transport models are developed for simulating the spatial and temporal distributions of the different compounds at the pipe level considering the effects of the dynamic hydraulics in the system driven by the spatiotemporal variability in water demands. System scale models are designed to optimize the operation of the different elements of the system by performing large-scale simulations coupled with optimization algorithms to identify the optimal operational strategies as a basis for accurate decision-making and superior water quality management. In conclusion, the computational models developed in this study can either be implemented as stand-alone tools for simulating the fundamental processes dictating the water quality at different scales of resolution, or be integrated into a unified framework in which information from the small scale models are propagated into the larger scale models to render a high fidelity representation of these processes

Identification and Quantification of Greenhouse Gas Emissions from Oil and Natural Gas Operations Using an Aircraft-Based Mass Balance Technique

Rapid advancements in horizontal drilling and hydraulic fracturing techniques have led to a booming natural gas industry. Natural gas is considered a cleaner fuel alternative to coal, producing less carbon dioxide (CO2) upon combustion per unit energy produced, and therefore has been hailed as a bridge fuel during conversion from fossil fuels to renewable energies for electricity production. The primary component of natural gas is methane (CH4), a potent greenhouse gas with 28 times the global warming potential of CO2 on a 100 year timescale. At oil and natural gas facilities, CH4 leaks are common due to changes in operational modes, scheduled ventings to relieve pressure from equipment, equipment aging, and malfunctions, and it is estimated that a CH4 leak rate of 1.5% of facility throughput is enough to negate the climate benefits incurred by use of natural gas instead of coal. Additionally, the Obama administration has set an aggressive mitigation goal of 26-28% emission reduction by 2025, as compared to 2005 levels. To achieve this target, emission sources must be quickly identified and quantified with high precision and accuracy to best understand where additional controls are required. Here, an aircraft-based measurement technique is used to address this challenge using a high-precision cavity ring-down spectroscopy system to measure atmospheric concentrations of CH4, CO2, and H2O, in conjunction with high-frequency three-dimensional wind measurements and aircraft location tracking from an onboard global positioning and inertial navigation system. Here, an assessment of method accuracy and precision was performed by conducting repeat measurements at a power plant and comparing the calculated CO2 emission rate to the reported hourly emissions measurements made by continuous emissions monitoring systems at the facility. Subsequently, results are presented from a field campaign conducted in the Barnett shale, Texas which quantified CH4 emissions from facilities with atypically large emissions, known as “super-emitters”, and assessed their overall contribution to basin-wide emissions. Calculated emissions were compared to inventory estimates and potential reasons for discrepancies were discussed. Results suggested that super-emitting facilities do not emit at the same rate for extended periods of time, and therefore, their emissions can vary by several orders of magnitude depending on operating conditions. To investigate the degree to which temporal variability of emissions occurs, a separate study was conducted in the Eagle Ford shale, Texas, in which four unique measurement methods were used to conduct repeat measurements at facilities during different operational modes. Results were assessed to suggest potential mitigation strategies that may address this variability to improve national inventories. Finally, a series of measurements were made at natural gas-fired power plants and oil refineries, two facility-types with minimal to no CH4 monitoring requirements due to presumption that they produce negligible CH4 emissions annually. Calculated CH4 and CO2 emission rates were reported and improved emissions factors were presented as an alternative to industry-used emissions factors. Additionally, the source of CH4 emissions was assessed by comparison of CH4 enhancements with combustion- and non-combustion-related enhancements

Air Quality Research Using Remote Sensing

Air pollution is a worldwide environmental hazard that poses serious consequences not only for human health and the climate but also for agriculture, ecosystems, and cultural heritage, among other factors. According to the WHO, there are 8 million premature deaths every year as a result of exposure to ambient air pollution. In addition, more than 90% of the world’s population live in areas where the air quality is poor, exceeding the recommended limits. On the other hand, air pollution and the climate co-influence one another through complex physicochemical interactions in the atmosphere that alter the Earth’s energy balance and have implications for climate change and the air quality. It is important to measure specific atmospheric parameters and pollutant compound concentrations, monitor their variations, and analyze different scenarios with the aim of assessing the air pollution levels and developing early warning and forecast systems as a means of improving the air quality and safeguarding public health. Such measures can also form part of efforts to achieve a reduction in the number of air pollution casualties and mitigate climate change phenomena. This book contains contributions focusing on remote sensing techniques for evaluating air quality, including the use of in situ data, modeling approaches, and the synthesis of different instrumentations and techniques. The papers published in this book highlight the importance and relevance of air quality studies and the potential of remote sensing, particularly that conducted from Earth observation platforms, to shed light on this topic