Accurate prediction of solar irradiation, particularly Diffuse Horizontal Irradiance (DHI), is essential for optimizing solar energy systems. This paper presents a hybrid framework that integrates satellite imagery and satellite-derived data to improve the precision of DHI forecasts. Two key datasets were employed: ground-based Solar Irradiation Measurement (SRM) and satellite-based Solar Irradiation (SSR). The proposed methodology utilizes machine learning models along two paths. The first path processes satellite imagery using advanced techniques, including pixel imputation with a modified Random Forest (RF) and Generative Adversarial Network (GAN), noisy region detection with Self-Organizing Maps (SOM), and noise removal through a Latent Diffusion Model (LDM). The second path handles tabular data through a diffusion probabilistic model designed for missing data imputation. These features are then merged and fed into a Long Short-Term Memory (LSTM) network, enhanced to capture seasonality and trends for accurate DHI predictions. The paper makes significant contributions by introducing a robust hybrid framework that leverages both satellite imagery and tabular data, incorporating novel preprocessing methods such as GAN-based pixel imputation and diffusion-based noise removal. The LSTM model is further adapted to handle seasonal and trend components, resulting in enhanced DHI forecasting accuracy. The proposed model demonstrates superior performance, achieving a Mean Squared Error (MSE) of 8.170 W/m2, Root Mean Squared Error (RMSE) of 1.749 W/m2, Mean Absolute Error (MAE) of 0.693 W/m2, and an R-squared value of 2.574, significantly outperforming conventional methods such as Artificial Neural Networks (ANN), Support Vector Regression (SVR), and traditional LSTM models. Additionally, the model records a low Mean Absolute Percentage Error (MAPE) of 0.8183% and a computation time of 35.4 s, highlighting its efficiency. These results contribute to more accurate and reliable solar energy resource management by offering enhanced DHI forecasts across various spatial and temporal scales.

A hybrid deep learning framework for solar irradiation prediction based on regional satellite images and data

Monitoring volcanic activity of barren Island, India using multi-sensor and thermal remote sensing

Partial differential equation (PDE)‐based spatio‐temporal models are available in the literature for modeling spatio‐temporal processes governed by advection‐diffusion equations. The main idea is to approximate the process by a truncated Fourier series and model the temporal evolution of the spectral coefficients by a stochastic process whose parametric structure is determined by the governing PDE. However, because many spatio‐temporal processes are nonperiodic with boundary discontinuities, the truncation of Fourier series leads to the well‐known Gibbs phenomenon (GP) in the output generated by the existing PDE‐based approaches. This article shows that the existing PDE‐based approach can be extended to suppress GP. The proposed approach starts with a data flipping procedure for the process respectively along the horizontal and vertical directions, as if we were unfolding a piece of paper folded twice along the two directions. For the flipped process, this article extends the existing PDE‐based spatio‐temporal model by obtaining the new temporal dynamics of the spectral coefficients. Because the flipped process is spatially periodic and has a complete waveform without boundary discontinuities, GP is removed even if the Fourier series is truncated. Numerical investigations show that the extended approach improves the modeling and prediction accuracy. Computer code is made available on GitHub.

An extended PDE‐based statistical spatio‐temporal model that suppresses the Gibbs phenomenon

A class of physics-informed spatio-temporal models has recently been proposed for modeling spatio-temporal processes governed by advection-diffusion equations. The central idea is to approximate the process by a truncated Fourier series and let the governing physics determine the dynamics of the spectral coefficients. However, because many spatio-temporal processes in real applications are non-periodic with boundary discontinuities, the well-known Gibbs phenomenon and ripple artifact almost always exist in the outputs generated by such models due to truncation of the Fourier series. Hence, the key contribution of this paper is to propose a physics-informed spatio-temporal modeling approach that significantly suppresses the Gibbs phenomenon when modeling spatio-temporal advection-diffusion processes. The proposed approach starts with a data flipping procedure for the process respectively along the horizontal and vertical directions (as if we were unfolding a piece of paper that has been folded twice along the two directions). Because the flipped process becomes spatially periodic and has a complete waveform without any boundary discontinuities, the Gibbs phenomenon disappears even if the Fourier series is truncated. Then, for the flipped process and given the Partial Differential Equation (PDE) that governs the process, this paper extends an existing PDE-based spatio-temporal model by obtaining the new temporal dynamics of the spectral coefficients, while maintaining the physical interpretation of the flipped process. Numerical investigations based on a real dataset have been performed to demonstrate the advantages of the proposed approach. It is found that the proposed approach effectively suppresses the Gibbs Phenomenon and significantly reduces the ripple artifact in modeling spatio-temporal advection-diffusion processes. Computer code is available on GitHub.

Gibbs Phenomenon Suppression in PDE-Based Statistical Spatio-Temporal Models

The 2019-20 Australia bushfire incurred numerous economic losses and significantly affected the operations of power systems. A power station or transmission line can be significantly affected due to bushfires, leading to an increase in operational costs. We study a fundamental but challenging problem of planning the optimal power flow (OPF) for power systems subject to bushfires. Considering the stochastic nature of bushfire spread, we develop a model to capture such dynamics based on Moore's neighborhood model. Under a periodic inspection scheme that reveals the in-situ bushfire status, we propose an online optimization modeling framework that sequentially plans the power flows in the electricity network. Our framework assumes that the spread of bushfires is non-stationary over time, and the spread and containment probabilities are unknown. To meet these challenges, we develop a contextual online learning algorithm that treats the in-situ geographical information of the bushfire as a 'spatial context'. The online learning algorithm learns the unknown probabilities sequentially based on the observed data and then makes the OPF decision accordingly. The sequential OPF decisions aim to minimize the regret function, which is defined as the cumulative loss against the clairvoyant strategy that knows the true model parameters. We provide a theoretical guarantee of our algorithm by deriving a bound on the regret function, which outperforms the regret bound achieved by other benchmark algorithms. Our model assumptions are verified by the real bushfire data from NSW, Australia, and we apply our model to two power systems to illustrate its applicability.

Online Planning of Power Flows for Power Systems Against Bushfires Using Spatial Context

On 22 December 2018, volcano Anak Krakatau, located in Indonesia, erupted and experienced a major lateral collapse. The triggered tsunami killed at least 437 people by the 13-m-high tide. Traditional optical imagery plays a great role in monitoring volcanic activities, but it is susceptible to cloud and fog interference and has low temporal resolution. Synthetic aperture radar (SAR) imagery can monitor volcanic activities at a high temporal resolution, and it is immune to the influence of clouds. In this paper, we propose an automatic method to accurately extract the volcano boundary from SAR images by combining multi-polarized water enhancement and the Nobuyuki Otsu (OTSU) method. We extract the area change of the volcano in 2018–2019 from Sentinel-1 images and ALOS-2 imagesThe area change and evolution are verified and analyzed by combing the results from SAR and optical data. The results show that the southeastern part of the volcano expanded significantly after the eruption, and the western part experienced collapse and recovery. The volcano morphology change experienced a slow-fast-slow process in the two years.

/pdf/revealing-the-morphological-evolution-of-krakatau-volcano-by-li4rv2mq.pdf

Revealing the Morphological Evolution of Krakatau Volcano by Integrating SAR and Optical Remote Sensing Images

Increasingly frequent wildfires have significant impact on solar energy production as the atmospheric aerosols generated by wildfires diminish the incoming solar radiation. Atmospheric aerosols can be measured by aerosol optical depth (AOD), and multiple spatiotemporal AOD data streams are available from geostationary satellites. Although these multisource remote-sensing data streams are measurements of the same underlying AOD process, they often present heterogeneous characteristics, such as different measurement biases and errors, data missing rates, etc. It is usually not known which data source provides more accurate measurements, and simply averaging multiple data streams is not always the best practice. Hence, this paper proposes a statistical model that is capable of inferring the underlying true AOD process by simultaneously integrating heterogeneous multisource remote-sensing data streams and the advection-diffusion equation that governs the dynamics of the AOD process. A bias correction process is included in the model to account for the bias of the physics model and the truncation error of the Fourier series. The proposed approach is applied to California wildfires AOD data obtained from the GOES East (GOES-16) and GOES West (GOES-17) satellites operated by the National Oceanic and Atmospheric Administration. Comprehensive numerical examples are provided to demonstrate the predictive capabilities and model the interpretability of the proposed approach. Funding: This work was funded by the U.S. National Science Foundation [Grant 2143695]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/ijds.2021.0058 . Data Ethics & Reproducibility Note: Our code requires packages that are not available on CodeOcean (for example, the Rfast package in R). Hence, code and sample data are provided on GitHub: https://github.com/gz-wei/Physics-Informed-Statistical-Modeling-for-Wildfire-Aerosols-Propagation .

A Statistical Model for Multisource Remote-Sensing Data Streams of Wildfire Aerosols Optimal Depth

Convolutional Non-homogeneous Poisson Process with Application to Wildfire Risk Quantification for Power Delivery Networks

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