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Fault detection in PV arrays and inverters is critical for ensuring maximum efficiency and performance. Artificial intelligence (AI) learning can be used to quickly identify issues, resulting in a sustainable environment with reduced downtime and maintenance costs. As the use of solar energy systems continues to grow, the need for reliable and efficient fault detection and diagnosis techniques becomes more critical. This paper presents a novel approach for fault detection in photovoltaic (PV) arrays and inverters, combining AI techniques. It integrates Elman neural network (ENN), boosted tree algorithms (BTA), multi-layer perceptron (MLP), and Gaussian processes regression (GPR) for enhanced accuracy and reliability in fault diagnosis. It leverages its strengths for the accuracy and reliability of fault diagnosis. Feature engineering-based sensitivity analysis was utilized for feature extraction. The fault detection and diagnosis were assessed using several statistical criteria including PBAIS, MAE, NSE, RMSE, and MAPE. Two intelligent learning scenarios are carried out. The first scenario is conducted for PV array fault detection with DC power (DCP) as output. The second scenario is conducted for inverter fault detection with AC power (ACP) as the output. The proposed technique is capable of detecting faults in PV arrays and inverters, providing a reliable solution for enhancing the performance and reliability of solar energy systems. A real-world solar energy dataset is used to evaluate the proposed technique with results compared to existing detection techniques and obtained results showing that it outperforms existing fault detection techniques, achieving higher accuracy and better performance. The GPR-M4 optimization justified its reliably among all the models with MAPE = 0.0393 and MAE = 0.002 for inverter fault detection, and MAPE = 0.091 and MAE = 0.000 for PV array fault detection. [ABSTRACT FROM AUTHOR]

HD 106315 and GJ 9827 are two bright, nearby stars that host multiple super-Earths and sub-Neptunes discovered by K2 that are well suited for atmospheric characterization. We refined the planets' ephemerides through Spitzer transits, enabling accurate transit prediction required for future atmospheric characterization through transmission spectroscopy. Through a multiyear high-cadence observing campaign with Keck/High Resolution Echelle Spectrometer and Magellan/Planet Finder Spectrograph, we improved the planets' mass measurements in anticipation of Hubble Space Telescope transmission spectroscopy. For GJ 9827, we modeled activity-induced radial velocity signals with a Gaussian process informed by the Calcium II H&K lines in order to more accurately model the effect of stellar noise on our data. We measured planet masses of Mb = 4.87 ± 0.37 M⊕, Mc = 1.92 ± 0.49 M⊕, and Md = 3.42 ± 0.62 M⊕. For HD 106315, we found that such activity radial velocity decorrelation was not effective due to the reduced presence of spots and speculate that this may extend to other hot stars as well (Teff > 6200 K). We measured planet masses of Mb = 10.5 ± 3.1 M⊕ and Mc = 12.0 ± 3.8 M⊕. We investigated all of the planets' compositions through comparison of their masses and radii to a range of interior models. GJ 9827 b and GJ 9827 c are both consistent with a 50/50 rock-iron composition, GJ 9827 d and HD 106315 b both require additional volatiles and are consistent with moderate amounts of water or hydrogen/helium, and HD 106315 c is consistent with a ∼10% hydrogen/helium envelope surrounding an Earth-like rock and iron core.

X-ray reverberation mapping is a powerful technique for probing the innermost accretion disk, whereas continuum reverberation mapping in the UV, optical, and infrared (UVOIR) reveals reprocessing by the rest of the accretion disk and broad-line region (BLR). We present the time lags of Mrk 817 as a function of temporal frequency measured from 14 months of high-cadence monitoring from Swift and ground-based telescopes, in addition to an XMM-Newton observation, as part of the AGN STORM 2 campaign. The XMM-Newton lags reveal the first detection of a soft lag in this source, consistent with reverberation from the innermost accretion flow. These results mark the first simultaneous measurement of X-ray reverberation and UVOIR disk reprocessing lags—effectively allowing us to map the entire accretion disk surrounding the black hole. Similar to previous continuum reverberation mapping campaigns, the UVOIR time lags arising at low temporal frequencies are longer than those expected from standard disk reprocessing by a factor of 2–3. The lags agree with the anticipated disk reverberation lags when isolating short-timescale variability, namely timescales shorter than the H β lag. Modeling the lags requires additional reprocessing constrained at a radius consistent with the BLR size scale inferred from contemporaneous H β -lag measurements. When we divide the campaign light curves, the UVOIR lags show substantial variations, with longer lags measured when obscuration from an ionized outflow is greatest. We suggest that, when the obscurer is strongest, reprocessing by the BLR elongates the lags most significantly. As the wind weakens, the lags are dominated by shorter accretion disk lags.

We present a new analysis of Kepler-21, the brightest ( V = 8.5) Kepler system with a known transiting exoplanet, Kepler-21 b. Kepler-21 b is a radius valley planet ( R = 1.6 ± 0.2 R _⊕ ) with an Earth-like composition (8.38 ± 1.62 g cm ^–3 ), though its mass and radius fall in the regime of possible “water worlds.” We utilize new Keck/High-Resolution Echelle Spectrometer and WIYN/NEID radial velocity (RV) data in conjunction with Kepler and Transiting Exoplanet Survey Satellite (TESS) photometry to perform a detailed study of activity mitigation between photometry and RVs. We additionally refine the system parameters, and we utilize Gaia astrometry to place constraints on a long-term RV trend. Our activity analysis affirms the quality of Kepler photometry for removing correlated noise from RVs, despite its temporal distance, though we reveal some cases where TESS may be superior. Using refined orbital parameters and updated composition curves, we rule out a water world scenario for Kepler-21 b, and we identify a long-period super-Jupiter planetary candidate, Kepler-21 (c).

Precise physical properties of the known transiting exoplanets are essential for their precise atmospheric characterization using modern and upcoming instruments. Leveraging the large volume of high-signal-to-noise-ratio photometric follow-up data from TESS, highly precise physical properties can be estimated for these systems, especially for those discovered using ground-based instruments prior to the TESS mission. In this work, I have used the publicly available TESS follow-up data for 28 transiting systems with 10 < V _mag < 10.5, with an aim to update their known physical properties. The observed lightcurves have been analyzed by implementing a state-of-the-art critical noise treatment algorithm to effectively reduce both time-correlated and uncorrelated noise components, using sophisticated techniques like wavelet denoising and Gaussian-process regression. Compared with the previous studies, the estimated transit parameters are found to be more precise for most of the targets, including a few cases where a larger space-based instrument like Spitzer, Kepler, or CHEOPS has been used in the previous study. The large volume of transit observations used for each target has also resulted in a more accurate estimation of the physical properties, as this overcomes any error in parameter estimations from bias present in a smaller volume of data. Thus, comparing with the literature values, statistically significant improvements in the known physical properties of several targeted systems have been reported from this work. The large volume of transit-timing information from the analyses was also used to search for transit-timing variation trends in these targets, which has resulted in no significant detection.

The detection of planetary transits in the light curves of active stars, featuring correlated noise in the form of stellar variability, remains a challenge. Depending on the noise characteristics, we show that the traditional technique that consists of detrending a light curve before searching for transits alters their signal-to-noise ratio and hinders our capability to discover exoplanets transiting rapidly rotating active stars. We present nuance , an algorithm to search for transits in light curves while simultaneously accounting for the presence of correlated noise, such as stellar variability and instrumental signals. We assess the performance of nuance on simulated light curves as well as on the Transiting Exoplanet Survey Satellite light curves of 438 rapidly rotating M dwarfs. For each data set, we compare our method to five commonly used detrending techniques followed by a search with the Box-Least-Squares algorithm. Overall, we demonstrate that nuance is the most performant method in 93% of cases, leading to both the highest number of true positives and the lowest number of false-positive detections. Although simultaneously searching for transits while modeling correlated noise is expected to be computationally expensive, we make our algorithm tractable and available as the JAX-powered Python package nuance, allowing its use on distributed environments and GPU devices. Finally, we explore the prospects offered by the nuance formalism and its use to advance our knowledge of planetary systems around active stars, both using space-based surveys and sparse ground-based observations.

Gaussian process (GP) regression is a nonparametric Bayesian approach that has been used successfully in various astronomical domains, especially in time-domain astronomy. The most common applications are the smoothing of data for interpolation and the detection of periodicities. The ability to create unbiased data-driven models without a predefined physical model can be a major advantage over conventional regression methods. Prior knowledge can be included by setting boundary conditions or constraining hyperparameter values, while unknown hyperparameters are optimized during the conditioning of the model. We have adapted and transformed previous approaches of GP regression and introduce three new applications for this regression method, especially in the context of stellar occultations: the modeling of occultation light curves, the correction of public JPL ephemerides of minor planets based on publicly available image data of the Zwicky Transient Facility, and the detection of natural satellites. We used data from observations of stellar occultations to validate the models and achieved promising results in all cases, and thus we confirmed the flexibility of GP regression models. Considering various existing use cases in addition to our novel applications, GP regression can be used to model diverse data sets addressing a wide range of problems. The accuracy of the model depends on the input data and on the set boundary conditions. Generally, high-quality data allow the usage of loose boundary conditions, while low-quality data require more restrictive boundary conditions to avoid overfitting.

Gyrochronology, the field of age dating stars using mainly their rotation periods and masses, is ideal for inferring the ages of individual main-sequence stars. However, due to the lack of physical understanding of the complex magnetic fields in stars, gyrochronology relies heavily on empirical calibrations that require consistent and reliable stellar age measurements across a wide range of periods and masses. In this paper, we obtain a sample of consistent ages using the gyro-kinematic age-dating method, a technique to calculate the kinematics ages of stars. Using a Gaussian process model conditioned on ages from this sample (∼1–14 Gyr) and known clusters (0.67–3.8 Gyr), we calibrate the first empirical gyrochronology relation that is capable of inferring ages for single, main-sequence stars between 0.67 and 14 Gyr. Cross-validating and testing results suggest our model can infer cluster and asteroseismic ages with an average uncertainty of just over 1 Gyr, and the inferred ages for wide binaries agree within 0.83 Gyr. With this model, we obtain gyrochronology ages for ∼100,000 stars within 1.5 kpc of the Sun with period measurements from Kepler and Zwicky Transient Facility and 384 unique planet host stars. A simple code is provided to infer gyrochronology ages of stars with temperature and period measurements.

There are more than 5000 confirmed and validated planets beyond the solar system to date, more than half of which were discovered by NASA’s Kepler mission. The catalog of Kepler’s exoplanet candidates has only been extensively analyzed under the assumption of white noise (i.i.d. Gaussian), which breaks down on timescales longer than a day due to correlated noise (point-to-point correlation) from stellar variability and instrumental effects. Statistical validation of candidate transit events becomes increasingly difficult when they are contaminated by this form of correlated noise, especially in the low-signal-to-noise (S/N) regimes occupied by Earth–Sun and Venus–Sun analogs. To diagnose small long-period, low-S/N putative transit signatures with few (roughly 3–9) observed transit-like events (e.g., Earth–Sun analogs), we model Kepler's photometric data as noise, treated as a Gaussian process, with and without the inclusion of a transit model. Nested sampling algorithms from the Python UltraNest package recover model evidences and maximum a posteriori parameter sets, allowing us to disposition transit signatures as either planet candidates or false alarms within a Bayesian framework.

The prediction of hydrological droughts is vital for surface and ground waters, reservoir levels, hydroelectric power generation, agricultural production, forest fires, climate change, and the survival of living things. This study aimed to forecast 1-month lead-time hydrological droughts in the Yesilirmak basin. For this purpose, support vector regression, Gaussian process regression, regression tree, and ensemble tree models were used alone and in combination with a discrete wavelet transform. Streamflow drought index values were used to determine hydrological droughts. The data were divided into 70% training (1969–1998) and 30% (1999–2011) testing. The performance of the models was evaluated according to various statistical criteria such as mean square error, root means square error, mean absolute error, and determination coefficient. As a result, it was determined that the prediction performance of the models obtained by decomposing into subcomponents with the discrete wavelet transform was optimal. In addition, the most effective drought-predicting model was obtained using the db10 wavelet and MGPR algorithm with mean squared error 0.007, root mean squared error 0.08, mean absolute error 0.04, and coefficient of determination (R2) 0.99 at station 1413. The weakest model was the stand-alone FGSV (RMSE 0.88, RMSE 0.94, MAE 0.76, R2 0.14). Moreover, it was revealed that the db10 main wavelet was more accurate in predicting short-term drought than other wavelets. These results provide essential information to decision-makers and planners to manage hydrological droughts in the Yesilirmak basin. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) data provides an appealing opportunity for all-weather day or night Earth surface monitoring. The European constellation Sentinel-1 (S1) consisting of S1-A and S1-B satellites offers a suitable revisit time and spatial resolution for the observation of croplands from space. The C-band radar backscatter is sensitive to vegetation structure changes and phenology as well as soil moisture and roughness. It also varies depending on the local incidence angle (LIA) of the SAR acquisition's geometry. The LIA backscatter dependency could therefore be exploited to improve the retrieval of the crop biophysical variables. The availability of S1 radar time-series data at distinct observation angles holds the feasibility to retrieve leaf area index (LAI) evolution considering spatiotemporal coverage of intensively cultivated areas. Accordingly, this research presents a workflow merging multi-date S1 smoothed data acquired at distinct LIA with a Gaussian processes regression (GPR) and a cross-validation (CV) strategy to estimate cropland LAI of irrigated winter wheat. The GPR-S1-LAI model was tested against in situ data of the 2020 winter wheat campaign in the irrigated valley of Colorador river, South of Buenos Aires Province, Argentina. We achieved adequate validation results for LAI with R C V 2 = 0.67 and RMSE C V = 0.88 m 2 m − 2 . The trained model was further applied to a series of S1 stacked images, generating temporal LAI maps that well reflect the crop growth cycle. The robustness of the retrieval workflow is supported by the associated uncertainties along with the obtained maps. We found that processing S1 smoothed imagery with distinct acquisition geometries permits accurate radar-based LAI modeling throughout large irrigated areas and in consequence can support agricultural management practices in cloud-prone agri-environments. [ABSTRACT FROM AUTHOR]

Gaussian processes regression (GPR)-aided nonlinear channel equalizer (CE) is experimentally demonstrated in a multi-level intensity modulation and direct detection fiber link. In this scheme, the GPR model is used to estimate the transmitted symbols or the corresponding nonlinear distortions after pre-processing. The experimental results show that GPR-aided nonlinear CE has better nonlinear tolerance than conventional linear and nonlinear filter-based CE. It is also shown that the GPR model in the nonlinear channel equalization process can be understood as an optimized single-layer neural network model with infinite width. Finally, we reveal the relationship between the key coefficients in GPR model and parameters in fiber link through both experiment and simulation.

Background and aims: The quantitative retrieval of soil organic carbon (SOC) storage, particularly for soils with a large potential for carbon sequestration, is of global interest due to its link with the carbon cycle and the mitigation of climate change. However, complex ecosystems with good soil qualities for SOC storage are poorly studied. Methods: The interrelation between SOC and various vegetation remote sensing drivers is understood to demonstrate the link between the carbon stored in the vegetation layer and SOC of the top soil layers. Based on the mapping of SOC in two horizons (0–30 cm and 30–60 cm) we predict SOC with high accuracy in the complex and mountainous heterogeneous páramo system in Ecuador. A large SOC database (in weight % and in Mg/ha) of 493 and 494 SOC sampling data points from 0–30 cm and 30–60 cm soil profiles, respectively, were used to calibrate GPR models using Sentinel-2 and GIS predictors (i.e., Temperature, Elevation, Soil Taxonomy, Geological Unit, Slope Length and Steepness (LS Factor), Orientation and Precipitation). Results: In the 0–30 cm soil profile, the models achieved a R2 of 0.85 (SOC%) and a R2 of 0.79 (SOC Mg/ha). In the 30–60 cm soil profile, models achieved a R2 of 0.86 (SOC%), and a R2 of 0.79 (SOC Mg/ha). Conclusions: The used Sentinel-2 variables (FVC, CWC, LCC/Cab, band 5 (705 nm) and SeLI index) were able to improve the estimation accuracy between 3–21% compared to previous results of the same study area. CWC emerged as the most relevant biophysical variable for SOC prediction. [ABSTRACT FROM AUTHOR]

Earth observation offers an unprecedented opportunity to monitor intensively cultivated areas providing key support to assess fertilizer needs and crop water uptake. Routinely, vegetation traits mapping can help farmers to monitor plant development along the crop's phenological cycle, which is particularly relevant for irrigated agricultural areas. The high spatial and temporal resolution of the Sentinel-2 (S2) multispectral instrument leverages the possibility to estimate leaf area index (LAI), canopy chlorophyll content (CCC), and vegetation water content (VWC) from space. Therefore, our study presents a hybrid retrieval workflow combining a physically-based strategy with a machine learning regression algorithm, i.e., Gaussian processes regression, and an active learning technique to estimate LAI, CCC and VWC of irrigated winter wheat. The established hybrid models of the three traits were validated against in-situ data of a wheat campaign in the Bonaerense valley, South of the Buenos Aires Province, Argentina, in the year 2020. We obtained good to highly accurate validation results with LAI: R 2 = 0.92, RMSE = 0.43 m 2 m − 2 , CCC: R 2 = 0.80, RMSE = 0.27 g m − 2 and VWC: R 2 = 0.75, RMSE = 416 g m − 2 . The retrieval models were also applied to a series of S2 images, producing time series along the seasonal cycle, which reflected the effects of fertilizer and irrigation on crop growth. The associated uncertainties along with the obtained maps underlined the robustness of the hybrid retrieval workflow. We conclude that processing S2 imagery with optimised hybrid models allows accurate space-based crop traits mapping over large irrigated areas and thus can support agricultural management decisions. [ABSTRACT FROM AUTHOR]

Rapid and accurate estimation of maize biomass is critical for predicting crop productivity. The launched Sentinel-1 (S-1) synthetic aperture radar (SAR) and Sentinel-2 (S-2) missions offer a new opportunity to map biomass. The selection of appropriate response variables is crucial for improving the accuracy of biomass estimation. We developed models from SAR polarization indices, vegetation indices (VIs), and biophysical variables (BPVs) based on gaussian process regression (GPR) and random forest (RF) with feature optimization to retrieve maize biomass in Changchun, Jilin province, Northeastern China. Three new predictors from each type of remote sensing data were proposed based on the correlations to biomass measured in June, July, and August 2018. The results showed that a predictor combined by vertical-horizontal polarization (VV), vertical-horizontal polarization (VH), and the difference of VH and VV (VH-VV) derived from S-1 images of June, July, and August, respectively, with GPR and RF, provided a more accurate estimation of biomass (R2 = 0.81–0.83, RMSE = 0.40–0.41 kg/m2) than the models based on single SAR polarization indices or their combinations, or optimized features (R2 = 0.04–0.39, RMSE = 0.84–1.08 kg/m2). Among the S-2 VIs, the GPR model using a combination of ratio vegetation index (RVI) of June, normalized different infrared index (NDII) of July, and normalized difference vegetation index (NDVI) of August achieved a result with R2 = 0.83 and RMSE = 0.39 kg/m2, much better than single VIs or their combination, or optimized features (R2 of 0.31–0.77, RMSE of 0.47–0.87 kg/m2). A BPV predictor, combined with leaf chlorophyll content (CAB) in June, canopy water content (CWC) in July, and fractional vegetation cover (FCOVER) in August, with RF, also yielded the highest accuracy (R2 = 0.85, RMSE = 0.38 kg/m2) compared to that of single BPVs or their combinations, or optimized subset. Overall, the three combined predictors were found to be significant contributors to improving the estimation accuracy of biomass with GPR and RF methods. This study clearly sheds new insights on the application of S-1 and S-2 data on maize biomass modeling. [ABSTRACT FROM AUTHOR]

In this study, a robotic hand control system was designed based on data gloves, aiming to provide more intuitive control and improved operational performance for a remote robotic hand. Compensation measures were proposed for the time lag effect on the remote-control system to address the input and feedback time delays of the remote robot system. A Smith predictor structure was modified by replacing the linear estimator with a recurrent neural network. A convolutional neural network was applied to the long short-term memory (LSTM) model, as it had a better convergence time and learning performance than the multi-layer perceptron model during training. The experimental results demonstrate that the control effect of this scheme is approximately 0.5 s faster than the normal Smith predictive control, proving its effectiveness. [ABSTRACT FROM AUTHOR]

Gaussian process regression is an interesting probabilistic nonparametric regression method. This method is flexible in describing data patterns. Unfortunately, in the case of big data, this method has expensive computational complexity because it involves inverse matrix operations. In this study, we introduce a partition resampling method to overcome the computational complexity by reducing the dimensions of the data. The purpose of this study is to obtain a Gaussian process regression model using the partition resampling method. This method builds a model from a portion of the data. Although the model uses some data, it has the same advantages as the overall data model. This method partitions the training data and then takes one random sample from each partition to obtain data of a certain size sample. The Gaussian process regression model is a collection of functions that have hyperparameter characteristics. This study predicts new data using a hyperparameter averaging approach. We apply our method on stock price data to show that the sample size of 60% with 20 replications produces a close MAPE value of the overall data model. The computation time required is five times faster than the overall data model. So, in this case, the partition resampling method can overcome the computational complexity by reducing the data dimensions to 60%. In other cases, this method can be used as an alternative to model time series data more effectively. [ABSTRACT FROM AUTHOR]

Among several indicators for river engineering sustainability, the longitudinal dispersion coefficient ( $ K_x $ ) is the main parameter that defines the transport of pollutants in natural streams. Accurate estimation of $ K_x $ has been challenging for hydrologists due to the high stochasticity and non-linearity of this hydraulic-environmental parameter. This study presents a new hybrid machine learning (ML) model integrating a Gaussian Process Regression (GPR) and an evolutionary feature selection (FS) approach (i.e. Covariance Matrix Adaptation Evolution Strategy (CMAES)) to estimate $ K_x $ in natural streams. The dataset consists of geometric and hydraulic river system parameters from 29 streams in the United States. The modeling results showed that the proposed model outperformed other models in the literature, producing more stable and accurate estimations. The FS approach evidenced the significance of the cross-sectional average flow velocity (U), channel width (B), and channel sinuosity σ to estimate the dispersion coefficient. In quantitative terms, the integrated GPR model with feature selection approach attained the minimum root mean square error ( $ {\rm RMSE} = 48.67 $ ) and maximum coefficient of determination ( $ R^2 = 0.95 $ ). The proposed hybrid evolutionary ML model arises as robust, flexible and reliable alternative computer aid technology for predicting the longitudinal dispersion coefficient in natural streams.

Among several indicators for river engineering sustainability, the longitudinal dispersion coefficient ( K x ) is the main parameter that defines the transport of pollutants in natural streams. Accurate estimation of K x has been challenging for hydrologists due to the high stochasticity and non-linearity of this hydraulic-environmental parameter. This study presents a new hybrid machine learning (ML) model integrating a Gaussian Process Regression (GPR) and an evolutionary feature selection (FS) approach (i.e. Covariance Matrix Adaptation Evolution Strategy (CMAES)) to estimate K x in natural streams. The dataset consists of geometric and hydraulic river system parameters from 29 streams in the United States. The modeling results showed that the proposed model outperformed other models in the literature, producing more stable and accurate estimations. The FS approach evidenced the significance of the cross-sectional average flow velocity (U), channel width (B), and channel sinuosity σ to estimate the dispersion coefficient. In quantitative terms, the integrated GPR model with feature selection approach attained the minimum root mean square error ( RMSE = 48.67) and maximum coefficient of determination ( R 2 = 0.95). The proposed hybrid evolutionary ML model arises as robust, flexible and reliable alternative computer aid technology for predicting the longitudinal dispersion coefficient in natural streams. [ABSTRACT FROM AUTHOR]

We address the problem of nonlinear dynamical system identification in state space formulation using an approximate Gaussian process (GP) regression framework where the basis for GP are learned from data. Approximate GP inference is used to address the high computational cost of exact GP frameworks, and is based on basis function expansion concept. We address the design of these basis functions using data and eigenvalue decomposition framework. The need for such design arises in practical applications where the collected data has outliers, discontinuities and other non-stationary characteristics which limit not only the applicability but also the performance of traditional GP framework with inherent stationary assumptions. Using the available data set, a kernel eigenvalue problem is formulated which is solved using Monte Carlo techniques to construct basis functions. Then, a low-rank approximation to the exact GP is obtained by an approach similar to kernel principle component analysis (k-PCA). The coefficients in the basis function expansion and other unknown parameters are learned from data using sequential Monte Carlo technique. The proposed GP framework for dynamical system identification is tested and validated against fixed basis framework using simulations and experimental data.

We study the prospects of Gaussian processes (GPs), a machine-learning (ML) algorithm, as a tool to reconstruct the Hubble parameter H ( z ) with two upcoming gravitational-wave (GW) missions, namely, the evolved Laser Interferometer Space Antenna (eLISA) and the Einstein Telescope (ET). Assuming various background cosmological models, the Hubble parameter has been reconstructed in a nonparametric manner with the help of a GP using realistically generated catalogs for each mission. The effects of early-time and late-time priors on the reconstruction of H ( z ), and hence on the Hubble constant ( H _0 ), have also been focused on separately. Our analysis reveals that a GP is quite robust in reconstructing the expansion history of the Universe within the observational window of the specific missions under consideration. We further confirm that both eLISA and ET would be able to provide constraints on H ( z ) and H _0 , which would be competitive to those inferred from current data sets. In particular, we observe that an eLISA run of a ∼10 yr duration with ∼80 detected bright siren events would be able to constrain H _0 as precisely as a ∼3 yr ET run assuming ∼1000 bright siren event detections. Further improvement in precision is expected for longer eLISA mission durations such as a ∼15 yr time frame having ∼120 events. Lastly, we discuss the possible role of these future GW missions in addressing the Hubble tension, for each model, on a case-by-case basis.

The Gaussian process (GP) has gained much attention in cosmology due to its ability to reconstruct cosmological data in a model-independent manner. In this study, we compare two methods for GP kernel selection: approximate Bayesian computation (ABC) rejection and nested sampling. We analyze three types of data: cosmic chronometer data, type Ia supernovae data, and gamma-ray burst data, using five kernel functions. To evaluate the differences between kernel functions, we assess the strength of evidence using Bayes factors. Our results show that, for ABC rejection, the Matérn kernel with ν = 5/2 (M52 kernel) outperformes the commonly used radial basis function (RBF) kernel in approximating all three data sets. Bayes factors indicate that the M52 kernel typically supports the observed data better than the RBF kernel but with no clear advantage over other alternatives. However, nested sampling gives different results, with the M52 kernel losing its advantage. Nevertheless, Bayes factors indicate no significant dependence of the data on each kernel.

UV and optical continuum reverberation mapping is a powerful tool for probing the accretion disk and inner broad-line region. However, recent reverberation mapping campaigns in the X-ray, UV, and optical have found lags consistently longer than those expected from the standard disk reprocessing picture. The largest discrepancy to date was recently reported in Mrk 335, where UV/optical lags are up to 12 times longer than expected. Here, we perform a frequency-resolved time lag analysis of Mrk 335, using Gaussian processes to account for irregular sampling. For the first time, we compare the Fourier frequency-resolved lags directly to those computed using the popular interpolated cross-correlation function method applied to both the original and detrended light curves. We show that the anticipated disk reverberation lags are recovered by the Fourier lags when zeroing in on the short-timescale variability. This suggests that a separate variability component is present on long timescales. If this separate component is modeled as reverberation from another region beyond the accretion disk, we constrain a size scale of roughly 15 lt-days from the central black hole. This is consistent with the size of the broad-line region inferred from H β reverberation lags. We also find tentative evidence for a soft X-ray lag, which we propose may be due to light travel time delays between the hard X-ray corona and distant photoionized gas that dominates the soft X-ray spectrum below 2 keV.

The Transiting Exoplanet Survey Satellite (TESS) follow-up of a large number of known transiting exoplanets provides a unique opportunity to study their physical properties more precisely. Being a space-based telescope, the TESS observations are devoid of any noise component resulting from the interference of Earth’s atmosphere. TESS also provides a greater probability to observe subsequent transit events owing to its longer uninterrupted time-series observations compared to ground-based telescopes. For the exoplanets around bright host stars in particular, TESS time-series observations provide high signal-to-noise ratio (S/N) lightcurves, which can be used for higher-precision studies for these exoplanets. In this work, I have studied the TESS transit photometric follow-up observations of 28 exoplanets around bright stars with V _mag ≤ 10. The already high-S/N lightcurves from TESS have been further processed with a critical noise-treatment algorithm, using the wavelet-denoising and the Gaussian-process regression techniques, to effectively reduce the noise components, both correlated and uncorrelated in time, which were then used to estimate the physical properties of these exoplanets. The study has resulted in very precise values for the physical properties of the target exoplanets, with the improvements in precision being significant for most of the cases compared to the previous studies. Also, since a comparatively large number of transit lightcurves from TESS observations were used to estimate these physical properties for each of the target exoplanets, which removes any bias due to the lack of sufficient data sets, these updated physical properties can be considered extremely accurate and reliable for future studies.

High-precision lightcurves combined with eclipse-mapping techniques can reveal the horizontal and vertical structure of a planet’s thermal emission and the dynamics of hot Jupiters. Someday, they even may reveal the surface maps of rocky planets. However, inverting lightcurves into maps requires an understanding of the planet, star, and instrumental trends because they can resemble the gradual flux variations as the planet rotates (i.e., partial phase curves). In this work, we simulate lightcurves with baseline trends and assess the impact on planet maps. Baseline trends can be erroneously modeled by incorrect astrophysical planet map features, but there are clues to avoid this pitfall in both the residuals of the lightcurve during eclipse and sharp features at the terminator of the planet. Models that use a Gaussian process or polynomial to account for a baseline trend successfully recover the input map even in the presence of systematics but with worse precision for the m = 1 spherical harmonic terms. This is also confirmed with the ThERESA eigencurve method where fewer lightcurve terms can model the planet without correlations between the components. These conclusions help aid the decision on how to schedule observations to improve map precision. If the m = 1 components are critical, such as measuring the east/west hot-spot shift on a hot Jupiter, better characterization of baseline trends can improve the m = 1 terms’ precision. For latitudinal north/south information from m ≠ 1 mapping terms, it is preferable to obtain high signal to noise at ingress/egress with more eclipses.

Radial velocity (RV) measurements of transiting multiplanet systems allow us to understand the densities and compositions of planets unlike those in the solar system. Kepler-102, which consists of five tightly packed transiting planets, is a particularly interesting system since it includes a super-Earth (Kepler-102d) and a sub-Neptune-sized planet (Kepler-102e) for which masses can be measured using RVs. Previous work found a high density for Kepler-102d, suggesting a composition similar to that of Mercury, while Kepler-102e was found to have a density typical of sub-Neptune size planets; however, Kepler-102 is an active star, which can interfere with RV mass measurements. To better measure the mass of these two planets, we obtained 111 new RVs using Keck/HIRES and Telescopio Nazionale Galileo/HARPS-N and modeled Kepler-102's activity using quasiperiodic Gaussian process regression. For Kepler-102d, we report a mass upper limit M _d < 5.3 M _⊕ (95% confidence), a best-fit mass M _d = 2.5 ± 1.4 M _⊕ , and a density ρ _d = 5.6 ± 3.2 g cm ^−3 , which is consistent with a rocky composition similar in density to the Earth. For Kepler-102e we report a mass M _e = 4.7 ± 1.7 M _⊕ and a density ρ _e = 1.8 ± 0.7 g cm ^−3 . These measurements suggest that Kepler-102e has a rocky core with a thick gaseous envelope comprising 2%–4% of the planet mass and 16%–50% of its radius. Our study is yet another demonstration that accounting for stellar activity in stars with clear rotation signals can yield more accurate planet masses, enabling a more realistic interpretation of planet interiors.

Synthetic aperture radar (SAR) data provides an appealing opportunity for all-weather day or night Earth surface monitoring. The European constellation Sentinel-1 (S1) consisting of S1-A and S1-B satellites offers a suitable revisit time and spatial resolution for the observation of croplands from space. The C-band radar backscatter is sensitive to vegetation structure changes and phenology as well as soil moisture and roughness. It also varies depending on the local incidence angle (LIA) of the SAR acquisition’s geometry. The LIA backscatter dependency could therefore be exploited to improve the retrieval of the crop biophysical variables. The availability of S1 radar time-series data at distinct observation angles holds the feasibility to retrieve leaf area index (LAI) evolution considering spatiotemporal coverage of intensively cultivated areas. Accordingly, this research presents a workflow merging multi-date S1 smoothed data acquired at distinct LIA with a Gaussian processes regression (GPR) and a cross-validation (CV) strategy to estimate cropland LAI of irrigated winter wheat. The GPR-S1-LAI model was tested against in situ data of the 2020 winter wheat campaign in the irrigated valley of Colorador river, South of Buenos Aires Province, Argentina. We achieved adequate validation results for LAI with RCV2 = 0.67 and RMSECV = 0.88 m2 m−2. The trained model was further applied to a series of S1 stacked images, generating temporal LAI maps that well reflect the crop growth cycle. The robustness of the retrieval workflow is supported by the associated uncertainties along with the obtained maps. We found that processing S1 smoothed imagery with distinct acquisition geometries permits accurate radar-based LAI modeling throughout large irrigated areas and in consequence can support agricultural management practices in cloud-prone agri-environments.

Earth observation offers an unprecedented opportunity to monitor intensively cultivated areas providing key support to assess fertilizer needs and crop water uptake. Routinely, vegetation traits mapping can help farmers to monitor plant development along the crop’s phenological cycle, which is particularly relevant for irrigated agricultural areas. The high spatial and temporal resolution of the Sentinel-2 (S2) multispectral instrument leverages the possibility to estimate leaf area index (LAI), canopy chlorophyll content (CCC), and vegetation water content (VWC) from space. Therefore, our study presents a hybrid retrieval workflow combining a physically-based strategy with a machine learning regression algorithm, i.e., Gaussian processes regression, and an active learning technique to estimate LAI, CCC and VWC of irrigated winter wheat. The established hybrid models of the three traits were validated against in-situ data of a wheat campaign in the Bonaerense valley, South of the Buenos Aires Province, Argentina, in the year 2020. We obtained good to highly accurate validation results with LAI: R2 = 0.92, RMSE = 0.43 m2 m−2, CCC: R2 = 0.80, RMSE = 0.27 g m−2 and VWC: R2 = 0.75, RMSE = 416 g m−2. The retrieval models were also applied to a series of S2 images, producing time series along the seasonal cycle, which reflected the effects of fertilizer and irrigation on crop growth. The associated uncertainties along with the obtained maps underlined the robustness of the hybrid retrieval workflow. We conclude that processing S2 imagery with optimised hybrid models allows accurate space-based crop traits mapping over large irrigated areas and thus can support agricultural management decisions.

Machine learning algorithms, in particular, kernel-based machine learning methods such as Gaussian processes regression (GPR) have shown to be promising alternatives to traditional empirical methods for retrieving vegetation parameters from remotely sensed data. However, the performance of GPR in predicting forest biophysical parameters has hardly been examined using full-spectrum airborne hyperspectral data. The main objective of this study was to evaluate the potential of GPR to estimate forest leaf area index (LAI) using airborne hyperspectral data. To achieve this, field measurements of LAI were collected in the Bavarian Forest National Park (BFNP), Germany, concurrent with the acquisition of the Fenix airborne hyperspectral images (400−2500 nm) in July 2017. The performance of GPR was further compared with three commonly used empirical methods (i.e., narrowband vegetation indices (VIs), partial least square regression (PLSR), and artificial neural network (ANN)). The cross-validated coefficient of determination (Rcv2) and root mean square error (RMSEcv) between the retrieved and field-measured LAI were used to examine the accuracy of the respective methods. Our results showed that using the entire spectral data (400−2500 nm), GPR yielded the most accurate LAI estimation (Rcv2 = 0.67, RMSEcv = 0.53 m2 m−2) compared to the best performing narrowband VIs SAVI2 (Rcv2 = 0.54, RMSEcv = 0.63 m2 m−2), PLSR (Rcv2 = 0.74, RMSEcv = 0.73 m2 m−2) and ANN (Rcv2 = 0.68, RMSEcv = 0.54 m2 m−2). Consequently, when a spectral subset obtained from the analysis of VIs was used as model input, the predictive accuracies were generally improved (GPR RMSEcv = 0.52 m2 m−2; ANN RMSEcv = 0.55 m2 m−2; PLSR RMSEcv = 0.69 m2 m−2), indicating that extracting the most useful information from vast hyperspectral bands is crucial for improving model performance. In general, there was an agreement between measured and estimated LAI using different approaches (p > 0.05). The generated LAI map for BFNP using GPR and the spectral subset endorsed the LAI spatial distribution across the dominant forest classes (e.g., deciduous stands were generally associated with higher LAI values). The accompanying LAI uncertainty map generated by GPR shows that higher uncertainties were observed mainly in the regions with low LAI values (low vegetation cover) and forest areas which were not well represented in the collected sample plots. This study demonstrated the potential of GPR for estimating LAI in forest stands using airborne hyperspectral data. Owing to its capability to generate accurate predictions and associated uncertainty estimates, GPR is evaluated as a promising candidate for operational retrieval applications of vegetation traits.

To study the influence of foliage density and distribution on spray droplets distribution inside plant canopy, the optical porosity of vertically stratified layers of cotton canopy, was proposed to quantitatively depict the spatial distribution of cotton foliage. For calculating stratification porosity, a laser ranging bench was built to obtain ranging data for all the stratified layers. For acquiring droplet distribution data inside plant canopy, spray experiments were carried out by using artificial cotton plants as targets and water sensitive papers as a deposit sampling method. Using stratification porosities and droplets distribution data as training data, Gaussian process (GP)-based models were established to quantitatively identify droplet spatial distribution inside the canopy. The trained models predicted droplet distribution of the upper (UL) and middle layers (ML) with mean absolute percentage errors (MAPE) 0.0226, R 2 0.9309 and MAPE 0.0526 and R 2 0.9039, respectively. Prediction accuracy for the lower layer (LL) was MAPE 0.1181, R 2 0.3361. Furthermore, for droplet distributions of the different sections in the same layer, prediction accuracies for UL and ML were obtained with MAPE 0.2038, R 2 0.9114 and MAPE 0.3641, R 2 0.822, respectively. For prediction accuracy of the LL was MAPE 0.2468, R2 0.4899. Considering the complex influence of all three foliage layers on droplets distribution of LL, and the horizontal velocity component of droplet movement, stratification porosity data could not explain droplets horizontal distribution in LL. Other description parameters should be included to improve model performance in future work. Moreover, the prediction model quantitatively revealed a uniform spray method could not produce a uniform deposition on the target, due to the interception of droplets by spatially inhomogeneous-distributed foliage. • GP-based prediction models of droplets distribution in cotton canopy built. • Stratification porosity used to depict foliage spatial distribution. • Models quantitatively reveal the influence of foliage on droplets distribution. [ABSTRACT FROM AUTHOR]

The integrating method of kernel partial least squares with Gaussian process regression (KPLS-GPR) was used to predict the effluent chemical oxygen demand (COD) and effluent suspended solids (SS) in papermaking wastewater treatment processes (WWTP). The latent variables from KPLS were used to handle the high dimensionality and the complex collinearity problem of WWTP data. The GPR model was used to develop the regression between the latent variables and the output variables. Based on the WWTP data set from a paper mill, the simulation experiment was conducted. The soft sensors of artificial neural network (ANN), PLS-ANN and KPLS-ANN were proposed for the comparison. The results show that the latent variables of KPLS can be used to improve the prediction results of conventional models obviously, and KPLS-GPR achieves the best prediction performance. For the prediction of effluent COD and effluent SS, the values of determination coefficient for KPLS-GPR are 0.575 and 0.610, respectively, which are respectively improved by 36.90% and 43.87% in comparison with those of the conventional counterparts. [ABSTRACT FROM AUTHOR]

Although Gaussian process regression (GPR) is a powerful Bayesian nonparametric regression model for engineering problems, its predictive performance is highly dependent on a kernel for covariance function of GPR. However, choosing a proper kernel is still challenging even for experts. To choose proper kernel automatically, this study proposes a compositional kernel (CPK) learning using tree-based genetic programming (GEP). The optimal structure of the kernel is defined as a compositional representation based on sums and products of eight base-kernels. The CPK can be encoded as a tree-structure, so that tree-based GEP is employed to discover an optimal tree-structure of the CPK. To avoid overly complex solution in GEP, the proposed method introduced a dynamic maximum tree-depth technique. The novelty of the proposed method is to utilize more flexible and efficient learning capability to learn the relationship between input and output than existing methods. To evaluate the learning capability of the proposed method, seven test functions were firstly investigated with various noise levels, and its predictive accuracy was compared with existing methods. Reliability problems in both parallel and series systems were introduced to evaluate the performance of the proposed method for efficient reliability assessment. The results show that the proposed method generally outperforms or performs similarly to the best one among existing methods. In addition, it is also shown that proper kernel function can significantly improve the performance of GPR as the training data increases. Stated differently, the proposed method can learn the function of being fitted efficiently with less training samples than existing methods. In this context, the proposed method can make powerful and automatic predictive modeling based on GPR in engineering problems. [ABSTRACT FROM AUTHOR]

The present paper deals with the applicability of the Meyer–Peter and Müller (MPM) bed load transport formula. The performance of the formula is examined on data collected in a particular location of Nestos River in Thrace, Greece, in comparison to a proposed Εnhanced MPM (EMPM) formula and to two typical machine learning methods, namely Random Forests (RF) and Gaussian Processes Regression (GPR). The EMPM contains new adjustment parameters allowing calibration. The EMPM clearly outperforms MPM and, also, it turns out to be quite competitive in comparison to the machine learning schemes. Calibrations are repeated with suitably smoothed measurement data and, in this case, EMPM outperforms MPM, RF and GPR. Data smoothing for the present problem is discussed in view of a special nearest neighbor smoothing process, which is introduced in combination with nonlinear regression.

In this study, the subject of investigation was the dynamic double pendulum crank mechanism used in a robotic arm. The arm is driven by a DC motor though the crank system and connected to a fixed side with a mount that includes a single spring and damping. Robotic arms are now widely used in industry, and the requirements for accuracy are stringent. There are many factors that can cause the induction of nonlinear or asymmetric behavior and even excite chaotic motion. In this study, bifurcation diagrams were used to analyze the dynamic response, including stable symmetric orbits and periodic and chaotic motions of the system under different damping and stiffness parameters. Behavior under different parameters was analyzed and verified by phase portraits, the maximum Lyapunov exponent, and Poincaré mapping. Firstly, to distinguish instability in the system, phase portraits and Poincaré maps were used for the identification of individual images, and the maximum Lyapunov exponents were used for prediction. GoogLeNet and ResNet-50 were used for image identification, and the results were compared using a convolutional neural network (CNN). This widens the convolutional layer and expands pooling to reduce network training time and thickening of the image; this deepens the network and strengthens performance. Secondly, the maximum Lyapunov exponent was used as the key index for the indication of chaos. Gaussian process regression (GPR) and the back propagation neural network (BPNN) were used with different amounts of data to quickly predict the maximum Lyapunov exponent under different parameters. The main finding of this study was that chaotic behavior occurs in the robotic arm system and can be more efficiently identified by ResNet-50 than by GoogLeNet; this was especially true for Poincaré map diagnosis. The results of GPR and BPNN model training on the three types of data show that GPR had a smaller error value, and the GPR-21 × 21 model was similar to the BPNN-51 × 51 model in terms of error and determination coefficient, showing that GPR prediction was better than that of BPNN. The results of this study allow the formation of a highly accurate prediction and identification model system for nonlinear and chaotic motion in robotic arms.

Abstract Foliar functional traits refer to a range of biochemical and physiognomic characteristics of vegetation that control photosynthesis, nutrient and water cycling, and can be used to describe the functional diversity of ecosystems. In this study, we utilize NASA AVIRIS - Next Generation imaging spectroscopy data to map 15 foliar functional traits in a grassland experiment at the Cedar Creek Ecosystem Science Reserve, a Long-Term Ecological Research site in Central Minnesota, across three years. To estimate traits, we compared the widely used partial least squares regression (PLSR) with Gaussian processes regression (GPR) to assess differences in model performance and uncertainty estimates. PLSR is attractive for its straightforward implementation and interpretation, but requires bootstrapping-based methods to estimate and map prediction uncertainties. On the other hand, GPR is more complex to implement and interpret, but provides explicit estimates of uncertainties. Our results indicated that foliar functional traits can be retrieved in these grasslands with moderate to high accuracies using either method. Highest validation accuracies were obtained for leaf mass per area (LMA), soluble cell contents, hemicellulose and cellulose (all with R 2 > 0.8), and lower accuracies for lignin, nitrogen, and some pigments with both techniques. The estimations for the mass-based pigments were more accurate than the area-based pigments (at least 5% improvement in normalized RMSE). Overall, GPR and PLSR performed comparably with respect to both skill of predictions and the selection of most informative spectral regions. Maps of uncertainties corresponded well between the two models, with highest uncertainties related to low vegetation cover, high diversity levels, or under irrigation and nitrogen treatments (not represented in the field sampling). The maps showed that trait values in each plot were relatively stable across three years of managed species richness. Our results provide a template for mapping foliar traits and their uncertainties in grasslands, and point to the need for extensive ground data across time to properly evaluate performance of trait mapping algorithms. Highlights • 15 foliar functional traits were mapped in a grassland experiment across three years. • The performances of GPR and PLSR were comparable. • Traits associated with leaf structure were more accurately estimated than pigments. • The trait maps and uncertainties generated by GPR and PLSR were consistent. • Plots with highest uncertainty were due to treatments or low vegetation cover. [ABSTRACT FROM AUTHOR]

The use of Gaussian processes (GPs) as models for astronomical time series data sets has recently become almost ubiquitous, given their ease of use and flexibility. In particular, GPs excel at marginalization over the stellar signal when the variability due to starspots is treated as a nuisance, as in exoplanet transit modeling. However, these effective models are less useful in cases where the starspot signal is of primary interest, since it is not obvious how the parameters of the GP relate to physical parameters like the spot size, contrast, and latitudinal distribution. Instead, it is common practice to explicitly model the effect of individual starspots on the light curve and attempt to infer their properties via optimization or posterior inference. Unfortunately, this process is ill-posed and often computationally intractable when applied to stars with more than a few spots and/or to ensembles of many stars. Here we derive a closed-form expression for a GP that describes the light curve of a rotating, evolving stellar surface conditioned on a given distribution of starspot sizes, contrasts, and latitudes. We demonstrate that this model is correctly calibrated, allowing one to robustly infer physical parameters of interest from one or more light curves, including the typical spot radii and latitudes. Our GP has far-ranging implications for understanding the variability and magnetic activity of stars from light curves and radial velocity measurements, as well as for modeling correlated noise in exoplanet searches. Our implementation is efficient, user-friendly, and open-source, available in the package starry _ process . https://github.com/rodluger/starry_process

Predicting the crop nitrogen (N) nutrition status is critical for optimizing nitrogen fertilizer application. The present study examined the ability of multiple image features derived from unmanned aerial vehicle (UAV) RGB images for winter wheat N status estimation across multiple critical growth stages. The image features consisted of RGB-based vegetation indices (VIs), color parameters, and textures, which represented image features of different aspects and different types. To determine which N status indicators could be well-estimated, we considered two mass-based N status indicators (i.e., the leaf N concentration (LNC) and plant N concentration (PNC)) and two area-based N status indicators (i.e., the leaf N density (LND) and plant N density (PND)). Sixteen RGB-based VIs associated with crop growth were selected. Five color space models, including RGB, HSV, L*a*b*, L*c*h*, and L*u*v*, were used to quantify the winter wheat canopy color. The combination of Gaussian processes regression (GPR) and Gabor-based textures with four orientations and five scales was proposed to estimate the winter wheat N status. The gray level co-occurrence matrix (GLCM)-based textures with four orientations were extracted for comparison. The heterogeneity in the textures of different orientations was evaluated using the measures of mean and coefficient of variation (CV). The variable importance in projection (VIP) derived from partial least square regression (PLSR) and a band analysis tool based on Gaussian processes regression (GPR-BAT) were used to identify the best performing image features for the N status estimation. The results indicated that (1) the combination of RGB-based VIs or color parameters only could produce reliable estimates of PND and the GPR model based on the combination of color parameters yielded a higher accuracy for the estimation of PND (R2val = 0.571, RMSEval = 2.846 g/m2, and RPDval = 1.532), compared to that based on the combination of RGB-based VIs; (2) there was no significant heterogeneity in the textures of different orientations and the textures of 45 degrees were recommended in the winter wheat N status estimation; (3) compared with the RGB-based VIs and color parameters, the GPR model based on the Gabor-based textures produced a higher accuracy for the estimation of PND (R2val = 0.675, RMSEval = 2.493 g/m2, and RPDval = 1.748) and the PLSR model based on the GLCM-based textures produced a higher accuracy for the estimation of PNC (R2val = 0.612, RMSEval = 0.380%, and RPDval = 1.601); and (4) the combined use of RGB-based VIs, color parameters, and textures produced comparable estimation results to using textures alone. Both VIP-PLSR and GPR-BAT analyses confirmed that image textures contributed most to the estimation of winter wheat N status. The experimental results reveal the potential of image textures derived from high-definition UAV-based RGB images for the estimation of the winter wheat N status. They also suggest that a conventional low-cost digital camera mounted on a UAV could be well-suited for winter wheat N status monitoring in a fast and non-destructive way.

Vertical Axis Wind Turbine (VAWT) blades experience stall conditions at lower tip speed ratios during rotation, resulting in inefficient power performance. The power performance can be augmented by improving the blade's aerodynamic efficiency using active or/and passive flow control mechanisms. In this research, the power performance of 2-D H-type VAWT is enhanced by employing an optimum cavity on the suction side of the NACA 0018 blade airfoil. The optimum cavity shape was found using the Genetic Algorithm coupled with the Gaussian Process Regression (GPR) model at an airfoil static stall angle of attack in an isolated environment to reduce the computational cost of the optimization process. Two GPR models were employed to predict lift and drag coefficients, while the lift-to-drag ratio was used as an objective function in the optimization algorithm. 80 CFD runs were utilized for initial training and testing of the models, which reduced computational effort by 97% compared to a pure CFD-based optimization approach. The aerodynamic efficiency of the optimum cavity shape predicted by GPR models was also confirmed by CFD simulation, which showed only a 0.5% difference. For an airfoil with an optimum cavity, the aerodynamic efficiency was consistent at lower angles of attack. However, a significant rise of up to 31.8% was observed in the near stall region between 12 ° to 16 ° angle of attack in comparison to clean airfoil. The optimum cavity on baseline VAWT blades enhanced power performance by 63.8% at a TSR of 1.5. Moreover, at TSRs of 2, 2.5, 3, and 3.5, the enhancement in power performance was achieved by 34.4%, 22.2%, 16.1%, and 3.2%, respectively. This demonstrates the potential of employing an optimum cavity on the suction side for performance augmentation without applying the suction and a cost-effective solution by conducting optimization in an isolated environment. • 63.8% improvement in power performance of 2-bladed VAWT using optimum cavity shape at TSR 1.5 • 31.8% improvement in aerodynamic efficiency of NACA 0018 at stall angle of attack using optimum cavity shape. • 97% reduction in computational effort for GPR-based optimization in comparison to CFD-based optimization strategy. • Gaussian Process Regression and Genetic Algorithm-based Airfoil Cavity Shape Optimization. [ABSTRACT FROM AUTHOR]

Leaf area index (LAI) estimates can inform decision-making in crop management. The European Space Agency’s Sentinel-2 satellite, with observations in the red-edge spectral region, can monitor crops globally at sub-field spatial resolutions (10–20 m). However, satellite LAI estimates require calibration with ground measurements. Calibration is challenged by spatial heterogeneity and scale mismatches between field and satellite measurements. Unmanned Aerial Vehicles (UAVs), generating high-resolution (cm-scale) LAI estimates, provide intermediary observations that we use here to characterise uncertainty and reduce spatial scaling discrepancies between Sentinel-2 observations and field surveys. We use a novel UAV multispectral sensor that matches Sentinel-2 spectral bands, flown in conjunction with LAI ground measurements. UAV and field surveys were conducted on multiple dates—coinciding with different wheat growth stages—that corresponded to Sentinel-2 overpasses. We compared chlorophyll red-edge index (CIred-edge) maps, derived from the Sentinel-2 and UAV platforms. We used Gaussian processes regression machine learning to calibrate a UAV model for LAI, based on ground data. Using the UAV LAI, we evaluated a two-stage calibration approach for generating robust LAI estimates from Sentinel-2. The agreement between Sentinel-2 and UAV CIred-edge values increased with growth stage—R2 ranged from 0.32 (stem elongation) to 0.75 (milk development). The CIred-edge variance between the two platforms was more comparable later in the growing season due to a more homogeneous and closed wheat canopy. The single-stage Sentinel-2 LAI calibration (i.e., direct calibration from ground measurements) performed poorly (mean R2 = 0.29, mean NRMSE = 17%) when compared to the two-stage calibration using the UAV data (mean R2 = 0.88, mean NRMSE = 8%). The two-stage approach reduced both errors and biases by >50%. By upscaling ground measurements and providing more representative model training samples, UAV observations provide an effective and viable means of enhancing Sentinel-2 wheat LAI retrievals. We anticipate that our UAV calibration approach to resolving spatial heterogeneity would enhance the retrieval accuracy of LAI and additional biophysical variables for other arable crop types and a broader range of vegetation cover types.

Remote-sensing-based machine learning approaches for water quality parameters estimation, Secchi Disk Depth (SDD) and Turbidity, were developed for the Valle de Bravo reservoir in central Mexico. This waterbody is a multipurpose reservoir, which provides drinking water to the metropolitan area of Mexico City. To reveal the water quality status of inland waters in the last decade, evaluation of MERIS imagery is a substantial approach. This study incorporated in-situ collected measurements across the reservoir and remote sensing reflectance data from the Medium Resolution Imaging Spectrometer (MERIS). Machine learning approaches with varying complexities were tested, and the optimal model for SDD and Turbidity was determined. Cross-validation demonstrated that the satellite-based estimates are consistent with the in-situ measurements for both SDD and Turbidity, with R2 values of 0.81 to 0.86 and RMSE of 0.15 m and 0.95 nephelometric turbidity units (NTU). The best model was applied to time series of MERIS images to analyze the spatial and temporal variations of the reservoir’s water quality from 2002 to 2012. Derived analysis revealed yearly patterns caused by dry and rainy seasons and several disruptions were identified. The reservoir varied from trophic to intermittent hypertrophic status, while SDD ranged from 0–1.93 m and Turbidity up to 23.70 NTU. Results suggest the effects of drought events in the years 2006 and 2009 on water quality were correlated with water quality detriment. The water quality displayed slow recovery through 2011–2012. This study demonstrates the usefulness of satellite observations for supporting inland water quality monitoring and water management in this region.

Image processing entered the era of artificial intelligence, and machine learning algorithms emerged as attractive alternatives for time series data processing. Satellite image time series processing enables crop phenology monitoring, such as the calculation of start and end of season. Among the promising algorithms, Gaussian process regression (GPR) proved to be a competitive time series gap-filling algorithm with the advantage of, as developed within a Bayesian framework, providing associated uncertainty estimates. Nevertheless, the processing of time series images becomes computationally inefficient in its standard per-pixel usage, mainly for GPR training rather than the fitting step. To mitigate this computational burden, we propose to substitute the per-pixel optimization step with the creation of a cropland-based precalculations for the GPR hyperparameters θ . To demonstrate our approach hardly affects the accuracy in fitting, we used Sentinel-2 LAI time series over an agricultural region in Castile and Leon, North-West Spain. The performance of image reconstructions were compared against the standard per-pixel GPR time series processing. Results showed that accuracies were on the same order (RMSE 0.1767 vs. 0.1564 [ m 2 / m 2 ] , 12% RMSE degradation) whereas processing time accelerated about 90 times. We further evaluated the alternative option of using the same hyperparameters for all the pixels within the complete scene. It led to similar overall accuracies over crop areas and computational performance. Crop phenology indicators were also calculated for the three different approaches and compared. Results showed analogous crop temporal patterns, with differences in start and end of growing season of no more than five days. To the benefit of crop monitoring applications, all the gap-filling and phenology indicators retrieval techniques have been implemented into the freely downloadable GUI toolbox DATimeS.

Energy consumption issues are important factors concerning the achievement of sustainable social development and also have a significant impact on energy security, particularly for China whose energy structure is experiencing a transformation. Construction of an accurate and reliable prediction model for the volatility changes in energy consumption can provide valuable reference information for policy makers of the government and for the energy industry. In view of this, a novel improved model is developed in this article by integrating the modified state transition algorithm (MSTA) with the Gaussian processes regression (GPR) approach for non-fossil energy consumption predictions for China at the end of the 13th Five-Year Project, in which the MSTA is utilized for effective optimization of hyper-parameters in GPR. Aiming for validating the superiority of MSTA, several comparisons are conducted on two well-known functions and the optimization results show the effectiveness of modification in the state transition algorithm (STA). Then, based on the latest statistical renewable energy consumption data, the MSTA-GPR model is utilized to generate consumption predictions for overall renewable energy and each single renewable energy source, including hydropower, wind, solar, geothermal, biomass and other energies, respectively. The forecasting results reveal that the proposed improved GPR can promote the forecasting ability of basic GPR and obtain the best prediction effect among all the other comparison models. Finally, combined with the forecasting results, the trend of each renewable energy source is analyzed.

Leaf Area Index (LAI) and chlorophyll content are strongly related to plant development and productivity. Spatial and temporal estimates of these variables are essential for efficient and precise crop management. The availability of open-access data from the European Space Agency’s (ESA) Sentinel-2 satellite—delivering global coverage with an average 5-day revisit frequency at a spatial resolution of up to 10 metres—could provide estimates of these variables at unprecedented (i.e., sub-field) resolution. Using synthetic data, past research has demonstrated the potential of Sentinel-2 for estimating crop variables. Nonetheless, research involving a robust analysis of the Sentinel-2 bands for supporting agricultural applications is limited. We evaluated the potential of Sentinel-2 data for retrieving winter wheat LAI, leaf chlorophyll content (LCC) and canopy chlorophyll content (CCC). In coordination with destructive and non-destructive ground measurements, we acquired multispectral data from an Unmanned Aerial Vehicle (UAV)-mounted sensor measuring key Sentinel-2 spectral bands (443 to 865 nm). We applied Gaussian processes regression (GPR) machine learning to determine the most informative Sentinel-2 bands for retrieving each of the variables. We further evaluated the GPR model performance when propagating observation uncertainty. When applying the best-performing GPR models without propagating uncertainty, the retrievals had a high agreement with ground measurements—the mean R2 and normalised root-mean-square error (NRMSE) were 0.89 and 8.8%, respectively. When propagating uncertainty, the mean R2 and NRMSE were 0.82 and 11.9%, respectively. When accounting for measurement uncertainty in the estimation of LAI and CCC, the number of most informative Sentinel-2 bands was reduced from four to only two—the red-edge (705 nm) and near-infrared (865 nm) bands. This research demonstrates the value of the Sentinel-2 spectral characteristics for retrieving critical variables that can support more sustainable crop management practices.

Parameter distribution estimation has long been a hot issue for the uncertainty quantification of environmental models. Traditional approaches such as MCMC (Markov Chain Monte Carlo) are prohibitive to be applied to large complex dynamic models because of the high computational cost of computing resources. To reduce the number of model evaluations required, we proposed an adaptive surrogate modeling-based sampling strategy for parameter distribution estimation, named ASMO-PODE (Adaptive Surrogate Modeling-based Optimization – Parameter Optimization and Distribution Estimation). The ASMO-PODE can provide an estimation of the parameter distribution using as little as one percent of the model evaluations required by a regular MCMC approach. The effectiveness and efficiency of the ASMO-PODE approach have been evaluated with 2 test problems and one land surface model, the Common Land Model. The results demonstrated that the ASMO-PODE method is an economic way for parameter optimization and distribution estimation. [ABSTRACT FROM AUTHOR]

The activated sludge process (ASP) is widely adopted to remove pollutants in wastewater treatment plants (WWTPs). However, the occurrence of filamentous sludge bulking often compromises the stable operation of the ASP. For timely diagnosis of filamentous sludge bulking for an activated sludge process in advance, this study proposed a Multi-Output Gaussian Processes Regression (MGPR) model for multi-step prediction and presented the Vector auto-regression (VAR) to learn the MGPR modelling deviation. The resulting models and associated uncertainty levels are used to monitor the filamentous sludge bulking related parameter, sludge volume index (SVI), such that the evolution of SVI can be predicted for both one-step and multi-step ahead. This methodology was validated with SVI data collected from one full-scale WWTP. Online diagnosis and prognosis of filamentous bulking sludge with real-time SVI prediction were tested through a simulation study. The results demonstrated that the proposed methodology was capable of predicting future SVI with good accuracy, thereby providing sufficient time for filamentous sludge bulking. [ABSTRACT FROM AUTHOR]