Related papers: A Cyberinfrastructure-based Approach to Real Time …
Big earth science data offers the scientific community great opportunities. Many more studies at large-scales, over long-terms and at high resolution can now be conducted using the rich information collected by remote sensing satellites,…
Climate change results in altered air and water temperatures. Increases affect physicochemical properties, such as oxygen concentration, and can shift species distribution and survival, with consequences for ecosystem functioning and…
Understanding environmental ecosystems is vital for the sustainable management of our planet. However,existing physics-based and data-driven models often fail to generalize to varying spatial regions and scales due to the inherent data…
Digital Twins have emerged as a disruptive technology with great potential; they can enhance WDS by offering real-time monitoring, predictive maintenance, and optimization capabilities. This paper describes the development of a…
A data-driven model for predicting the surface temperature using neural networks was proposed to alleviate the computational burden of numerical weather prediction (NWP). Our model, named TPTNet uses only 2m temperature measured at the…
Heat, Ventilation and Air Conditioning (HVAC) systems play a critical role in maintaining a comfortable thermal environment and cost approximately 40% of primary energy usage in the building sector. For smart energy management in buildings,…
This manuscript presents a novel state-of-the-art cyber-physical water testbed, namely: The AI and Cyber for Water and Agriculture testbed (ACWA). ACWA is motivated by the need to advance water supply management using AI and Cybersecurity…
Ensuring safe water supplies requires effective water quality monitoring, especially in developing countries like Nepal, where contamination risks are high. This paper introduces various hybrid deep learning models to predict on the CCME…
Water pollution is a critical issue that can affects humans' health and the entire ecosystem thus inducing economical and social concerns. In this paper, we focus on an Internet of Things water quality prediction system, namely WaterS, that…
Temperature determines the relative probability of observing a physical system in an energy state when that system is energetically in equilibrium with its environment. In this paper, we present a theory for engineering the temperature of a…
In this paper, we attempt to employ convolutional recurrent neural networks for weather temperature estimation using only image data. We study ambient temperature estimation based on deep neural networks in two scenarios a) estimating…
Measuring the performance of solar energy and heat transfer systems requires a lot of time, economic cost and manpower. Meanwhile, directly predicting their performance is challenging due to the complicated internal structures. Fortunately,…
Groundwater supports ecosystems, agriculture, and drinking water supplies worldwide, yet effective monitoring remains challenging due to sparse data, computational constraints, and delayed outputs from traditional approaches. We develop a…
Modeling and verifying real-world cyber-physical systems is challenging, which is especially so for complex systems where manually modeling is infeasible. In this work, we report our experience on combining model learning and abstraction…
A climate network represents the global climate system by the interactions of a set of anomaly time-series. Network science has been applied on climate data to study the dynamics of a climate network. The core task and first step to enable…
This paper proposes a physics-guided machine learning approach that combines advanced machine learning models and physics-based models to improve the prediction of water flow and temperature in river networks. We first build a recurrent…
The escalating impacts of climate change and the increasing demand for sustainable development and natural resource management necessitate innovative technological solutions. Quantum computing (QC) has emerged as a promising tool with the…
Recent years have witnessed a rise in the frequency and intensity of cyberattacks targeted at critical infrastructure systems. This study designs a versatile, data-driven cyberattack detection platform for infrastructure systems…
Most water in the universe may be superionic, and its thermodynamic and transport properties are crucial for planetary science but difficult to probe experimentally or theoretically. We use machine learning and free energy methods to…
In this paper, we introduce a novel framework for combining scientific knowledge within physics-based models and recurrent neural networks to advance scientific discovery in many dynamical systems. We will first describe the use of outputs…