Reduction In Mean Absolute Error Research Articles

Consumption–Production Profile Categorization in Energy Communities

Energy Transition is changing the renewable energy participation in new distributed generation systems like the Local Energy Markets. Due to its inherent intermittent and variable nature, forecasting production and consumption load profiles will be more challenging and demand more complex predictive models. This paper analyzes the production, consumption load profile, and storage headroom% of the Cornwall Local Energy Market, using advanced statistical time series methods to optimize the opportunity market the storage units provide. These models also help the Energy Community storage reserves to meet contract conditions with the Distribution Network Operator. With this more accurate and detailed knowledge, all sites from this Local Energy Market will benefit more from their installation by optimizing their energy consumption, production, and storage. This better accuracy will make the Local Energy Market more fluid and safer, creating a flexible system that will guarantee the technical quality of the product for the whole community. The training of several SARIMAX, Exponential Smoothing, and Temporal Causal models improved the fitness of consumption, production, and headroom% time series. These models properly decomposed the time series in trend, seasonality, and stochastic dynamic components that help us to understand how the Local Energy Market consumes, produces, and stores energy. The model design used all power flows and battery energy storage system state-of-charge site characteristics at daily and hourly granularity levels. All model building follows an analytical methodology detailed step by step. A benchmark between these sequence models and the incumbent forecasting models utilized by the Energy Community shows a better performance measured with model error reduction. The best models present mean squared error reduction between 88.89% and 99.93%, while the mean absolute error reduction goes from 65.73% to 97.08%. These predictive models built at different prediction scales will help the Energy Communities better contribute to the Network Management and optimize their energy and power management performance. In conclusion, the expected outcome of these implementations is a cost-optimal management of the Local Energy Market and its contribution to the needed new Flexibility Electricity System Scheme, extending the adoption of renewable energies.

Ring artifacts correction for computed tomography image using unsupervised contrastive learning

Objective. Computed tomography (CT) is a widely employed imaging technology for disease detection. However, CT images often suffer from ring artifacts, which may result from hardware defects and other factors. These artifacts compromise image quality and impede diagnosis. To address this challenge, we propose a novel method based on dual contrast learning image style transformation network model (DCLGAN) that effectively eliminates ring artifacts from CT images while preserving texture details. Approach. Our method involves simulating ring artifacts on real CT data to generate the uncorrected CT (uCT) data and transforming them into strip artifacts. Subsequently, the DCLGAN synthetic network is applied in the polar coordinate system to remove the strip artifacts and generate a synthetic CT (sCT). We compare the uCT and sCT images to obtain a residual image, which is then filtered to extract the strip artifacts. An inverse polar transformation is performed to obtain the ring artifacts, which are subtracted from the original CT image to produce a corrected image. Main results. To validate the effectiveness of our approach, we tested it using real CT data, simulated data, and cone beam computed tomography images of the patient’s brain. The corrected CT images showed a reduction in mean absolute error by 12.36 Hounsfield units (HU), a decrease in root mean square error by 18.94 HU, an increase in peak signal-to-noise ratio by 3.53 decibels (dB), and an improvement in structural similarity index by 9.24%. Significance. These results demonstrate the efficacy of our method in eliminating ring artifacts and preserving image details, making it a valuable tool for CT imaging.

A hybrid deep learning framework for unsteady periodic flow field reconstruction based on frequency and residual learning

Reconstructing a complete flow field from limited sensor measurement is quite essential for state evaluation, optimization, monitoring, and control of the flow system. Unsteady periodic flow, as a widespread phenomenon in science and engineering, attracts in-depth research over decades. Deep learning has been employed in flow field reconstruction, whereas the accurate estimation for the unsteady flow field with strong nonlinearity is still difficult. To address this problem, we propose a hybrid deep learning framework that incorporates frequency and residual learning to accurately reconstruct an unsteady periodic flow field from limited sensor measurement. First, to extract the frequency features, the historical flow field data is decomposed into different modes with different frequencies named frequency modes via fast Fourier transform (FFT). Next, we construct a hybrid deep neural network framework consisting of an inverse fast Fourier transform (IFFT) block and a residual block. The IFFT block maps sensor measurements to frequency mode temporal coefficients, which are multiplied with frequency modes to recover an IFFT field. Meanwhile, the residual block adaptively generates a residual field to complement the information lost by the IFFT field. Finally, the IFFT field and residual field are combined to produce the final reconstructed flow field. We conduct numerical experiments on the unsteady periodic flow around a cylinder and transonic flow around a NACA0012 airfoil to demonstrate the feasibility and high accuracy of our proposed method. Compared to the widely used proper orthogonal decomposition (POD) and shallow decoder (SD) methods, our approach achieves at least 83.9% and 72.2% reduction in mean absolute error, respectively.

Multi-Level Constrained Intra and Inter Subject Feature Representation for Facial Video Based BVP Signal Measurement.

Facial video-based blood volume pulse (BVP) signal measurement holds great potential for remote health monitoring, while existing methods have issues with convolutional kernel perceptual field constraints. This article proposes an end-to-end multi-level constrained spatiotemporal representation structure for facial video-based BVP signal measurement. First, an intra- and inter-subject feature representation is proposed to strengthen the BVP-related features generation at high, semantic, and shallow levels, respectively. Second, the global-local association is presented to enhance BVP signal period pattern learning, and the global temporal features are introduced into the local spatial convolution of each frame by adaptive kernel weights. Finally, the multi-dimensional fused features are mapped to one-dimensional BVP signals by the task-oriented signal estimator. The experimental results on the publicly available MMSE-HR dataset demonstrate that the proposed structure overperforms state-of-the-art methods (e.g., AutoHR) in BVP signal measurement, with a 20% and 40% reduction in mean absolute error and root mean squared error, respectively. The proposed structure would be a powerful tool for telemedical and non-contact heart health monitoring.

Enhanced Crash Frequency Models Using Surrogate Safety Measures from Connected Vehicle Fleet

As connected vehicle data became available, efforts to employ surrogate safety measures (SSMs) for crash frequency modeling were undertaken. For road safety evaluation, traffic conflicts are quantitatively measured by various SSMs in two dimensions: spatial/temporal proximity (e.g., time-to-collision, TTC) and evasive action (e.g., deceleration rate, DR). However, a single SSM or a single dimension only represents partial images of the true severity of traffic conflicts. Therefore, this study investigates possible enhancements in crash frequency modeling by concurrently using proximity and evasion SSMs. For rear-end crash frequency estimation, five negative binomial regression models and two tree-based models (a regression tree and random forest) were developed. All models were estimated using crashes, traffic volume, and segment length, along with three SSMs (DR, TTC, and modified TTC) extracted from connected vehicle data in Ann Arbor, Michigan. Results show that the multi-SSM model produced a 19.3% reduction in mean absolute error (MAE) compared to the baseline model with no SSM variable, which was significantly higher than those of single-SSM models (4.5−6.9% reductions in MAE). Between all models, the random forest, which is the ensemble machine learning model, produced the highest error reductions (a 44.3% reduction in MAE). These findings show that the concurrent use of proximity and evasion SSMs can yield further enhancements in crash frequency models compared to the singular use of either type of SSM. The proposed modeling method can be used for proactive safety management and assessment using connected vehicle data collected over a short period.

Harmonic Vibrational Frequency Simulation of Pharmaceutical Molecules via a Novel Multi-Molecular Fragment Interception Method.

By means of a computational method based on Density Functional Theory (DFT), using commercially available software, a novel method for simulating equilibrium geometry harmonic vibrational frequencies is proposed. Finasteride, Lamivudine, and Repaglinide were selected as model molecules to study the adaptability of the new method. Three molecular models, namely the single-molecular, central-molecular, and multi-molecular fragment models, were constructed and calculated by Generalized Gradient Approximations (GGAs) with the PBE functional via the Material Studio 8.0 program. Theoretical vibrational frequencies were assigned and compared to the corresponding experimental data. The results indicated that the traditional single-molecular calculation and scaled spectra with scale factor exhibited the worst similarity for all three pharmaceutical molecules among the three models. Furthermore, the central-molecular model with a configuration closer to the empirical structure resulted in a reduction of mean absolute error (MAE) and root mean squared error (RMSE) in all three pharmaceutics, including the hydrogen-bonded functional groups. However, the improvement in computational accuracy for different drug molecules using the central-molecular model for vibrational frequency calculation was unstable. Whereas, the new multi-molecular fragment interception method showed the best agreement with experimental results, exhibiting MAE and RMSE values of 8.21 cm-1 and 18.35 cm-1 for Finasteride, 15.95 cm-1 and 26.46 cm-1 for Lamivudine, and 12.10 cm-1 and 25.82 cm-1 for Repaglinide. Additionally, this work provides comprehensive vibrational frequency calculations and assignments for Finasteride, Lamivudine, and Repaglinide, which have never been thoroughly investigated in previous research.

A multi-terrain feature-based deep convolutional neural network for constructing super-resolution DEMs

Scale conversion between DEMs is an important issue in geomorphometry. There are many mature studies on the generation of low-resolution(LR) DEMs from high-resolution(HR) DEMs. However, as an important and convenient means of obtaining HR DEMs, traditional super resolution (SR) methods have shown insufficient consideration of the terrain features embedded in DEMs. Therefore, this article investigates the combination of terrain features and the use of convolutional neural networks (CNN) to reconstruct HR DEMs, and proposes a multi-terrain feature-based deep CNN for super-resolution(SR) DEMs (MTF-SR). In the experiments, from the perspective of vector and raster terrain features, we fuse raster terrain features in the input and loss functions, and fuse vector terrain features in the optimization of the output of the model. The results show that the MTF-SR model has a 30–50 % reduction in mean absolute error (MAE) compared with interpolation methods, has the lowest slope and aspect error and has a 10 to 30 % improvement in streamline matching rate (SMR). These results point to the advantages of the method in overall elevation accuracy and the preservation of terrain features.

Learning under concept drift and non-stationary noise: Introduction of the concept of persistence

Learning from noisy data is a challenging task especially when the system under consideration has a non-stationary nature. The source of the noise is often assumed to be stationary, however the severity or characteristics of noise may also be time-varying, which causes multiple sources of drift in the collected data. This study introduces a novel adaptive learning rate approach to improve learning when the observations from a non-stationary system is altered by an also non-stationary source of noise. As an example to this approach, we propose Persistence Aware Robust Learner (PeARL), an online learning method that utilizes a novel concept called persistence, which is a local noisiness estimation metric to measure the correspondence of a signal to discrete white noise. Making use of this metric, PeARL is able to adaptively adjust the learning rate for each observation during learning to reduce the effect of noise. With this level of control on the learning rate, noisy instances have less disruptive effect on the maintained estimate. We experimentally evaluate PeARL on (a) systematically generated synthetic data and (b) real-world data, including accelerometer readings collected from people (HASC2010corpus) and current measurements from electric motors collected within the scope of EU-funded research project iRel40. The experiments reveal a favorable region of noise rate, in which the proposed method achieves up to 40% reduction in mean absolute error (MAE).

A Method for Merging Multi-Source Daily Satellite Precipitation Datasets and Gauge Observations over Poyang Lake Basin, China

Obtaining precipitation estimates with high resolution and high accuracy is critically important for regional meteorological, hydrological, and other applications. Although satellite precipitation products can provide precipitation fields at various scales, their applications are limited by the relatively coarse spatial resolution and low accuracy. In this study, we propose a multi-source merging approach for generating accurate and high-resolution precipitation fields on a daily time scale. Specifically, a random effects eigenvector spatial filtering (RESF) method was first applied to downscale satellite precipitation datasets. The RESF method, together with Kriging, was then applied to merge the downscaled satellite precipitation products with station observations. The results were compared against observations and a data fusion dataset, the Multi-Source Weighted-Ensemble Precipitation (MSWEP). It was shown that the estimates of the proposed method significantly outperformed the individual satellite precipitation product, reducing the average value of mean absolute error (MAE) by 52%, root mean square error (RMSE) by 63%, and improving the mean value of Kling–Gupta efficiency (KGE) by 157%, respectively. Daily precipitation estimates exhibited similar spatial patterns to the MSWEP products, and were more accurate in almost all cases, with a 42% reduction in MAE, 46% reduction in RMSE, and 79% improvement in KGE. The proposed approach provides a promising solution to generate accurate daily precipitation fields with high spatial resolution.

Simultaneous multi-crop land suitability prediction from remote sensing data using semi-supervised learning

Land suitability models for Canada are currently based on single-crop inventories and expert opinion. We present a data-driven multi-layer perceptron that simultaneously predicts the land suitability of several crops in Canada, including barley, peas, spring wheat, canola, oats, and soy. Available crop yields from 2013–2020 are downscaled to the farm level by masking the district level crop yield data to focus only on areas where crops are cultivated and leveraging soil-climate-landscape variables obtained from Google Earth Engine for crop yield prediction. This new semi-supervised learning approach can accommodate data from different spatial resolutions and enables training with unlabelled data. The incorporation of a crop indicator function further allows for the training of a multi-crop model that can capture the interdependences and correlations between various crops, thereby leading to more accurate predictions. Through k-fold cross-validation, we show that compared to the single crop models, our multi-crop model could produce up to a 2.82 fold reduction in mean absolute error for any particular crop. We found that barley, oats, and mixed grains were more tolerant to soil-climate-landscape variations and could be grown in many regions of Canada, while non-grain crops were more sensitive to environmental factors. Predicted crop suitability was associated with a region’s growing season length, which supports climate change projections that regions of northern Canada will become more suitable for agricultural use. The proposed multi-crop model could facilitate assessment of the suitability of northern lands for crop cultivation and be incorporated into cost-benefit analyses.

A Stacking Ensemble Model of Various Machine Learning Models for Daily Runoff Forecasting

Improving the accuracy and stability of daily runoff prediction is crucial for effective water resource management and flood control. This study proposed a novel stacking ensemble learning model based on attention mechanism for the daily runoff prediction. The proposed model has a two-layer structure with the base model and the meta model. Three machine learning models, namely random forest (RF), adaptive boosting (AdaBoost), and extreme gradient boosting (XGB) are used as the base models. The attention mechanism is used as the meta model to integrate the output of the base model to obtain predictions. The proposed model is applied to predict the daily inflow to Fuchun River Reservoir in the Qiantang River basin. The results show that the proposed model outperforms the base models and other ensemble models in terms of prediction accuracy. Compared with the XGB and weighted averaging ensemble (WAE) models, the proposed model has a 10.22% and 8.54% increase in Nash–Sutcliffe efficiency (NSE), an 18.52% and 16.38% reduction in root mean square error (RMSE), a 28.17% and 18.66% reduction in mean absolute error (MAE), and a 4.54% and 4.19% increase in correlation coefficient (r). The proposed model significantly outperforms the base model and simple stacking model indicated by both the Friedman test and the Nemenyi test. Thus, the proposed model can produce reasonable and accurate prediction of the reservoir inflow, which is of great strategic significance and application value in formulating the rational allocation and optimal operation of water resources and improving the breadth and depth of hydrological forecasting integrated services.

Chaotic van der Pol Oscillator Control Algorithm Comparison

The damped van der Pol oscillator is a chaotic non-linear system. Small perturbations in initial conditions may result in wildly different trajectories. Controlling, or forcing, the behavior of a van der Pol oscillator is difficult to achieve through traditional adaptive control methods. Connecting two van der Pol oscillators together where the output of one oscillator, the driver, drives the behavior of its partner, the responder, is a proven technique for controlling the van der Pol oscillator. Deterministic artificial intelligence is a feedforward and feedback control method that leverages the known physics of the van der Pol system to learn optimal system parameters for the forcing function. We assessed the performance of deterministic artificial intelligence employing three different online parameter estimation algorithms. Our evaluation criteria include mean absolute error between the target trajectory and the response oscillator trajectory over time. Two algorithms performed better than the benchmark with necessary discussion of the conditions under which they perform best. Recursive least squares with exponential forgetting had the lowest mean absolute error overall, with a 2.46% reduction in error compared to the baseline, feedforward without deterministic artificial intelligence. While least mean squares with normalized gradient adaptation had worse initial error in the first 10% of the simulation, after that point it exhibited consistently lower error. Over the last 90% of the simulation, deterministic artificial intelligence with least mean squares with normalized gradient adaptation achieved a 48.7% reduction in mean absolute error compared to baseline.

Deep Learning-Based Road Traffic Noise Annoyance Assessment.

With the development of urban road traffic, road noise pollution is becoming a public concern. Controlling and reducing the harm caused by traffic noise pollution have been the hot spots of traffic noise management research. The subjective annoyance level of traffic noise has become one of the most important measurements for evaluating road traffic pollution. There are subjective experimental methods and objective prediction methods to assess the annoyance level of traffic noise: the subjective experimental method usually uses social surveys or listening experiments in laboratories to directly assess the subjective annoyance level, which is highly reliable, but often requires a lot of time and effort. The objective method extracts acoustic features and predicts the annoyance level through model mapping. Combining the above two methods, this paper proposes a deep learning model-based objective annoyance evaluation method, which directly constructs the mapping between the noise and annoyance level based on the listening experimental results and realizes the rapid evaluation of the noise annoyance level. The experimental results show that this method has reduced the mean absolute error by 30% more than the regression algorithm and neural network, while its performance is insufficient in the annoyance interval where samples are lacking. To solve this problem, the algorithm adopts transfer learning to further improve the robustness with a 30% mean absolute error reduction and a 5% improvement in the correlation coefficient between the true results and predicted results. Although the model trained on college students' data has some limitations, it is still a useful attempt to apply deep learning to noise assessment.

AFFIRM: Affinity Fusion-Based Framework for Iteratively Random Motion Correction of Multi-Slice Fetal Brain MRI.

Multi-slice magnetic resonance images of the fetal brain are usually contaminated by severe and arbitrary fetal and maternal motion. Hence, stable and robust motion correction is necessary to reconstruct high-resolution 3D fetal brain volume for clinical diagnosis and quantitative analysis. However, the conventional registration-based correction has a limited capture range and is insufficient for detecting relatively large motions. Here, we present a novel Affinity Fusion-based Framework for Iteratively Random Motion (AFFIRM) correction of the multi-slice fetal brain MRI. It learns the sequential motion from multiple stacks of slices and integrates the features between 2D slices and reconstructed 3D volume using affinity fusion, which resembles the iterations between slice-to-volume registration and volumetric reconstruction in the regular pipeline. The method accurately estimates the motion regardless of brain orientations and outperforms other state-of-the-art learning-based methods on the simulated motion-corrupted data, with a 48.4% reduction of mean absolute error for rotation and 61.3% for displacement. We then incorporated AFFIRM into the multi-resolution slice-to-volume registration and tested it on the real-world fetal MRI scans at different gestation stages. The results indicated that adding AFFIRM to the conventional pipeline improved the success rate of fetal brain super-resolution reconstruction from 77.2% to 91.9%.

Transpulmonary thermodilution: A revised correction formula for global end-diastolic volume index derived after femoral indicator injection.

Transpulmonary thermodilution (TPTD) is usually performed by jugular indicator injection. In clinical practice, femoral venous access is often used instead, resulting in substantial overestimation of global end-diastolic volume index (GEDVI). A correction formula compensates for that. The objective of this study is to first evaluate the efficacy of the currently implemented correction function and then further improve this formula. The performance of the established correction formula was investigated in our prospectively collected dataset of 98 TPTD measurements from 38 patients with both, jugular and femoral venous access. Subsequently, a new correction formula was developed: cross validation revealed the favourite covariate combination and a general estimating equation provided the final version, which was tested in a retrospective validation on an external dataset. Investigating the current correction function revealed a considerable reduction of bias compared to no correction. Concerning the objective of formula development, the covariate combination of GEDVI obtained after femoral indicator injection, age and body surface area is even favoured, when compared to the parameters of the previously published correction formula, as a further reduction of mean absolute error (68 vs. 61 ml/m2), a better correlation (0.90 vs. 0.91) and an increased adjusted R2 (0.72 vs 0.78) is noticed in the cross validation results. Of particular clinical importance is, that more measurements were correctly assigned to the same GEDVI category (decreased / normal / increased) using the revised formula, compared with the gold standard of jugular indicator injection (72.4 vs. 74.5%). In a retrospective validation, the newly developed formula showed a greater reduction of bias (to 2 vs. 6 %) than the currently implemented formula. The currently implemented correction function partly compensates for GEDVI overestimation. Applying the new correction formula on GEDVI measured after femoral indicator administration enhances the informative value and reliability of this preload parameter.

Adaptive Fusion and Transfer Learning for Enhanced E –Commerce Recommendations

Adaptive Fusion and Transfer Learning for Enhanced E-commerce Recommendations (AFETLER) is a holistic combination of some state-of-the-art recommendation frameworks. Most of the existing frameworks, be it Matrix Factorisation model, Deep learning-based model, Factorisation Machines and Two tower recommendation models, used in real world recommendation engines lack diversity and serendipity. Also, cold start problem is one that downgrade the recommendation algorithm. Defining the optimisation goal and closed feedback improvement loop is also something that is not given primary weightage.To overcome the above stated challenges and provide a novel approach to overcome those, we propose the AFETLER model. AFETLER incorporates an adaptive fusion technique that dynamically combines recommendations from several sources such as new arrivals forecast, Click-Through Rate (CTR) prediction, and popularity estimation. This fusion technique improves accuracy by giving varying weights to each source based on the user's context, history preferences, and current behaviour, resulting in highly relevant and tailored buying recommendations. To address the cold-start problem and improve recommendations for new items, AFETLER employs a transfer learning mechanism inspired by the Adversarial Two-tower Neural Network (ATNN) framework, utilizing knowledge from existing items' interactions to extract valuable features from new items' profiles. Furthermore, AFETLER leverages hierarchical attention methods to effectively capture user-item interaction patterns, boosting feature extraction and overall recommendation quality through dynamic weighing of interaction relevance.Proposed AFETLER model outperforms some of the existing recommendation models by an average 2%-5% on metrics such a precision, ROC-AUC, and Top-K recommendation accuracy. Further, we also see point reduction in Mean absolute error and mean squared error.

Performance of Machine Learning Algorithms considering Spatial Effects Assessment for Indoor Personal Thermal Comfort in Air-Conditioned Workplace

Personal comfort models were developed to circumvent most of the constraints imposed by the Predicted Mean Vote (PMV) and present adaptive models, which consider the average response of a large population. Although there has been a lot of research into new input features for personal comfort models, the spatial data of the building, such as windows, doors, furniture, walls, fans, and heating, ventilation, and air conditioning (HVAC) systems, (the location of its occupants with those elements), have not been thoroughly examined. This paper investigates the impact of the spatial parameter in predicting personal indoor thermal comfort using various machine learning approaches in air-conditioning offices under hot and humid climates. The Decision Tree, Random Forest, Support Vector Machine, K-Nearest Neighbour, and Neural Network were trained using a field study dataset that was done in nineteen office spaces yielding 628 samples from 42 occupants. The dataset is divided randomly into training and testing datasets, with a ratio of 80% and 20%. This study examines how well machine learning predicts personal thermal comfort with spatial data compared to without spatial data; where the spatial parameters have shown a significant influence on model prediction accuracies, Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). The result shows the average MAE is decreased by 10.6% with the Random Forest (RF) getting the most MAE reduction by 23.8%. Meanwhile, the average RMSE is reduced by 11.8% with the RF giving the most RMSE cutback by 30.6%. Consequently, the spatial effect analysis also determines which area of the room has cold or heat clusters area that affects thermal comfort that contributes to the design of sustainable buildings.

Upgrade of LSA-SAF Meteosat Second Generation daily surface albedo (MDAL) retrieval algorithm incorporating aerosol correction and other improvements

Abstract. MDAL is the operational Meteosat Second Generation (MSG)-derived daily surface albedo product that has been generated and disseminated in near real time by EUMETSAT Satellite Application Facility for Land Surface Analysis (LSA-SAF) since 2005. We propose and evaluate an update to the MDAL retrieval algorithm which introduces the accounting for aerosol effects as well as other scientific developments: pre-processing recalibration of radiances acquired by the SEVIRI instrument aboard MSG and improved coefficients for atmospheric correction as well as for albedo conversion from narrow- to broadband. We compare the performance of MDAL broadband albedos pre- and post-upgrade with respect to three types of reference data: the EPS Ten-Day Albedo product ETAL is used as the primary reference, while albedo derived from in situ flux measurements acquired by ground stations and MODIS MCD43D albedo data are used to complete the validation. For the comparison to ETAL – conducted over the whole coverage area of SEVIRI – we see a reduction in average white-sky albedo mean bias error (MBE) from −0.02 to negligible levels (<0.001) and a reduction in average mean absolute error (MAE) from 0.034 to 0.026 (−24 %). Improvements can be seen for black-sky albedo as well, albeit less pronounced (14 % reduction in MAE). Further analysis distinguishing individual seasons, regions and land covers show that performance changes have spatial and temporal dependence: for white-sky albedo we see improvements over almost all regions and seasons relative to ETAL, except for Eurasia in winter; resolved by land cover we see a similar effect with improvements for all types for all seasons except winter, where some types exhibit slightly worse results (crop-, grass- and shrublands). For black-sky albedo we similarly see improvements for all seasons when averaged over the full data set, although sub-regions exhibit clear seasonal dependence: the performance of the upgraded MDAL version is generally diminished in local winter but better in local summer. The comparison with in situ observations is less conclusive due to the well-known problem of the spatial representativeness of near-ground observations with respect to satellite pixel footprint sizes. Comparison with MODIS at the same locations shows mixed results in terms of change in performance following the proposed upgrade but proves the good quality of the MDAL products in general. Based on the evidence presented in this study, we consider the updated algorithm version to be able to deliver a valuable improvement of the operational MDAL product. This improvement is two-fold: primarily, there is the refinement of the albedo values themselves; secondarily, the increased alignment with the ETAL product is beneficial for those who wish to exploit synergies between EUMETSAT's geostationary and polar satellites to generate data sets based on the LSA-SAF albedo products from the two different missions.

SMART ENVIRONMENT INDEX PREDICTION OF SMART CITY USING POLYNOMIAL REGRESSION

Smart Environment refers to environment where pollution is detected, predicted, classified and solved using smart tools and technology such as using Internet of Things (IoT) sensors, cloud service, and machine learning algorithms. Episodic environment index prediction allows governments and local city agency to detect pollution in environment at a premature stage to initiate proper steps. Advancements in machine learning, sensor, and camera technology have endorsed us to do that, which can benefit millions of cities and its citizens indeed. This paper describes a new method for smart environment index prediction based on index of five others smart city components using polynomial regression. This research mainly works with the Smart Cities Index dataset. We attempted numerous steps to select feature and to filter its contents to make them more apposite for selected regression algorithm. Our approach unites several feature selection techniques, outlier detection technique, polynomial degree selection technique, and random state selection technique, which outcomes in a reduction of Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) to 0.0000000140, 0.000000000000000322, and 0.0000000179 respectively and rise R2 to 1.0 for prediction. This method can be a valuable tool for smart environment index prediction from smartness of various components of a city, thus drastically reducing survey or other related works.

Impact of environmental factors on heat-associated mortalities in an urban desert region

The troubling trend of rising heat-associated mortalities in an urban desert region (Maricopa County, AZ, USA) has motivated us to explore the extent to which environmental factors may contribute to increased heat-health risks. Summertime data from 2010 to 2019 were used to construct a suite of models for daily heat-associated mortalities. The best-performing full model included the following predictors, ordered from strongest to weakest influence: daily average air temperature, average of previous 5 days daily average air temperature, year, day of year, average of previous 5 days daily average dew point temperature, average of previous 5 days daily average PM2.5, and daily average PM10. This full model exhibited a 5.39% reduction in mean absolute error in daily heat-associated mortalities as compared to the best-performing model that included only air temperature as an environmental predictor. The extent to which issued and modeled excessive heat warnings (from both the temperature only and full models) corresponded with heat-associated mortalities was also examined. Model hindcasts for 2020 and 2021 showed that the models were able to capture the high number of heat-associated mortalities in 2020, but greatly undercounted the highest yet observed number of heat-associated mortalities in 2021. Results from this study lend insights into environmental factors corresponding to an increased number of heat-associated mortalities and can be used for informing strategies towards reducing heat-health risks. However, as the best-performing model was unable to fully capture the observed number of heat-associated mortalities, continued scrutiny of both environmental and non-environmental factors affecting these observations is needed.