Machine learning prediction of pyrolytic products of lignocellulosic biomass based on physicochemical characteristics and pyrolysis conditions

Experimental validation of multi-stage optimal energy management for a smart microgrid system under forecasting uncertainties

A dryness index TSWDI based on land surface temperature, sun-induced chlorophyll fluorescence, and water balance

Accurate prediction of Land Surface Temperature (LST) is critical for understanding and mitigating the effects of climate change and land use dynamics. This study proposes a novel approach that leverages ensemble models and correlation analysis based on Landsat-8 satellite data to forecast LST and explore its environmental relationships. Time-series satellite data spanning winter and summer seasons of 2018–2019 was retrieved from the Google Earth Engine (GEE) platform. LST, normalized difference vegetation index (NDVI), rainfall, and evapotranspiration (ET) datasets were derived from Landsat-8 data within GEE to facilitate LST modeling. The ensemble framework combines three powerful machine learning algorithms: XG-Boost, Bagging-XG-Boost, and AdaBoost, to enhance the accuracy and robustness of LST predictions. Compared to standalone models, the proposed ensemble models demonstrated significant improvements in LST prediction accuracy. While XG-Boost and AdaBoost achieved moderate accuracies with R2 values of 0.57 and 0.60, respectively, the Bagging ensemble model surpassed them with an outstanding R2 of 0.75. Furthermore, a correlation analysis by using linear regression (LR) model explored the relationships between ET, rainfall, NDVI, and LST. The analysis revealed strong positive correlations between NDVI and ET (R2 = 0.95), while correlations between NDVI and LST (R2 = 0.31) and NDVI and rainfall (R2 = 0.47) were weaker. These findings contribute significantly to our understanding of LST trends and the impact of climate change on environmental variables. Ultimately, this knowledge can inform effective sustainable decision-making in the area. 

Predictive modeling of land surface temperature (LST) based on Landsat-8 satellite data and machine learning models for sustainable development

A trustworthy assessment of soil moisture content plays a significant role in irrigation planning and in controlling various natural disasters such as floods, landslides, and droughts. Various machine learning models (MLMs) have been used to increase the accuracy of soil moisture content prediction. The present investigation aims to apply MLMs with novel structures for the estimation of daily volumetric soil water content, based on the stacking of the multilayer perceptron (MLP), random forest (RF), and support vector regression (SVR). Two groups of input variables were considered: the first (Model A) consisted of various meteorological variables (i.e., daily precipitation, air temperature, humidity, and wind speed), and the second (Model B) included only daily precipitation. The stacked model (SM) had the best performance (R2 = 0.962) in the prediction of daily volumetric soil water content for both categories of input variables when compared with the MLP (R2 = 0.957), RF (R2 = 0.956) and SVR (R2 = 0.951) models. Overall, the SM, which, in general, allows the weaknesses of the individual basic algorithms to be overcome while still maintaining a limited number of parameters and short calculation times, can lead to more accurate predictions of soil water content than those provided by more commonly employed MLMs.

/pdf/a-stacked-machine-learning-algorithm-for-multi-step-ahead-8ax6qzlx.pdf

A Stacked Machine Learning Algorithm for Multi-Step Ahead Prediction of Soil Moisture

Comparison of bagging, boosting and stacking algorithms for surface soil moisture mapping using optical-thermal-microwave remote sensing synergies

Ensemble surface soil moisture estimates at farm-scale combining satellite-based optical-thermal-microwave remote sensing observations

The present work investigated, the effect of heat treatment on tribological behaviour of 06% C steel Samples were austenitized at 910 ℃ for 1 h in a muffle furnace and then cooling was done at different cooling rate, like furnace cooling (annealing) and air cooling (normalizing) Followed by microstructural characterization was carried out using optical microscopy and XRD analysis Vickers hardness tester was used to evaluate hardness of the samples Wear behavior of the as-received and heat treated samples were evaluated by pin on disc wear tester The microstructure showed that presence of predominantly pearlite grains and less ferrite grains in as received and heat treated sample Phase analysis showed the ferrite and carbide phase in as received and heat treated sample Micro-hardness value decreased from 548 HV for as received 06% C steel to 256 HV for annealed sample and 412 HV for normalized sample The more wear resistance of as received sample as compared to annealed and normalized sample The wear depth was 20 µm for as received, on the other hand, 390 µm for normalized sample and 410 µm for annealed sample The coefficient of friction of as received sample varies from 0052 to 0062, on the other hand, coefficient of friction varies from 0052 to 0062 for normalized sample and 0062 to 0073 for annealed sample

Tribological performance of heat treated 0.6% c steel

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Comparison of bagging, boosting and stacking algorithms for surface soil moisture mapping using optical-thermal-microwave remote sensing synergies

Ensemble surface soil moisture estimates at farm-scale combining satellite-based optical-thermal-microwave remote sensing observations

Tribological performance of heat treated 0.6% c steel