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Aiming at the problem that the energy consumption of the boiler system varies greatly under the flexible peaking requirements of coal-fired units, an energy consumption prediction model for the boiler system is established based on a Least-Squares Support Vector Machine (LSSVM). First, the Mean Impact Value (MIV) algorithm is used to simplify the input characteristics of the model and determine the key operating parameters that affect energy consumption. Secondly, the Snow Ablation Optimizer (SAO) with tent map, adaptive t-distribution, and the opposites learning mechanism is introduced to determine the parameters in the prediction model. On this basis, based on the operation data of an ultra-supercritical coal-fired unit in Xinjiang, China, the boiler energy consumption dataset under variable load is established based on the theory of fuel specific consumption. The proposed prediction model is used to predict and analyze the boiler energy consumption, and a comparison is made with other common prediction methods. The results show that compared with the LSSVM, BP, and ELM prediction models, the average Relative Root Mean Squared Errors (aRRMSE) of the LSSVM model using ISAO are reduced by 2.13%, 18.12%, and 40.3%, respectively. The prediction model established in this paper has good accuracy. It can predict the energy consumption distribution of the boiler system of the ultra-supercritical coal-fired unit under variable load more accurately. [ABSTRACT FROM AUTHOR]

The research on landslide displacement prediction can help the early warning and prevention of landslide disasters in mining areas. In view of the problem that BP neural network is prone to local convergence and considering that the network trained based on time-series cumulative displacement may produce large errors in prediction, this paper proposes a method combining displacement increment and CS-BP (Cuckoo Search-Back Propagation) neural network to predict landslide displacement. Compared with the conventional landslide displacement prediction methods, this method uses displacement increment instead of the commonly used cumulative displacement as the network input data, selects the CS algorithm with fewer parameters and easy to implement to optimize the BP network to construct the prediction model, and predicts the corresponding amount of displacement change at the next moment by the historical landslide displacement increment. Combined with the measured data of three feature points of a mine in Xinjiang, China, obtained by the micro-deformation monitoring radar, the displacement prediction accuracy of the proposed model on the three measured data sets is compared with the prediction accuracy of the BP, GA-BP (Genetic Algorithm, GA), and FA-BP (Firefly Algorithm, FA) network prediction models based on cumulative displacement and incremental displacement, respectively. The experimental results show that this method achieves superior performance with an average root mean square error of 0.3261 and an average mean absolute error of 0.2785 across the three feature points, outperforming the other models, and holds promising applications in disaster prevention and control work. [ABSTRACT FROM AUTHOR]

Background: Reasonable and accurate forecasting of outpatient visits helps hospital managers optimize the allocation of medical resources, facilitates fine hospital management, and is of great significance in improving hospital efficiency and treatment capacity. Methods: Based on conjunctivitis outpatient data from the First Affiliated Hospital of Xinjiang Medical University Ophthalmology from 2017/1/1 to 2019/12/31, this paper built and evaluated Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models for outpatient visits prediction. Results: In predicting the number of conjunctivitis visits over the next 31 days, the LSTM model had a root mean square error (RMSE) of 2.86 and a mean absolute error (MAE) of 2.39, the GRU model has an RMSE of 2.60 and an MAE of 1.99. Conclusions: The GRU method can better predict trends in hospital outpatient flow over time, thus providing decision support for medical staff and outpatient management. [ABSTRACT FROM AUTHOR]

Wind energy, as a renewable energy source, offers the advantage of clean and pollution-free power generation. Its abundant resources have positioned wind power as the fastest-growing and most widely adopted method of electricity generation. Wind speed stands as a key characteristic when studying wind energy resources. This study primarily focuses on predictive models for wind speed in wind energy generation. The intense intermittency, randomness, and uncontrollability of wind speeds in wind power generation present challenges, leading to high development costs and posing stability challenges to power systems. Consequently, scientifically forecasting wind speed variations becomes imperative to ensure the safety of wind power equipment, maintain grid integration of wind power, and ensure the secure and stable operation of power systems. This holds significant guiding value and significance for power production scheduling institutions. Due to the complexity of wind speed, scientifically predicting its fluctuations is crucial for ensuring the safety of wind power equipment, maintaining wind power integration systems, and ensuring the secure and stable operation of power systems. This research aims to enhance the accuracy and stability of wind speed prediction, thereby reducing the costs associated with wind power generation and promoting the sustainable development of renewable energy. This paper utilizes an improved Hilbert–Huang transform (HHT) using complementary ensemble empirical mode decomposition (CEEMD) to overcome issues in the traditional empirical mode decomposition (EMD) method, such as component mode mixing and white noise interference. Such an approach not only enhances the efficiency of wind speed data processing but also better accommodates strong stochastic and nonlinear characteristics. Furthermore, by employing mathematical analytical methods to compute weights for each component, a dynamic neural network model is constructed to optimize wind speed time series modeling, aiming for a more accurate prediction of wind speed fluctuations. Finally, the optimized HHT-NAR model is applied in wind speed forecasting within the Xinjiang region, demonstrating significant improvements in reducing root mean square errors and enhancing coefficient of determination. This model not only showcases theoretical innovation but also exhibits superior performance in practical applications, providing an effective predictive tool within the field of wind energy generation. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Using Earth observation to accurately extract snow phenology changes is of great significance for deepening the understanding of the ecological environment and hydrological process, agricultural and animal husbandry production, and high-quality development of the social economy in Xinjiang. Considering snow cover phenology based on MODIS product MOD10A1 data, this paper constructed a method for automatically extracting key phenological parameters in Xinjiang and calculated three key phenological parameters in Xinjiang from 2001 to 2020, including SCD (snow cover duration), SOD (snow onset date), and SED (snow end date). The daily data of four field camera observation points during an overlapping period from 2017 to 2019 were used to evaluate the snow cover phenological parameters extracted by MOD10A1, and the mean absolute error (MAE) and root mean square error (RMSE) values were 0.65 and 1.07, respectively. The results showed the following: 1. The spatiotemporal variation in snow phenology was highly altitude dependent. The mean gradients of SCD in the Altai Mountains, Tienshan Mountains, and Kunlun Mountains is 2.6, 2.1, and 1.2 d/100 m, respectively. The variation trend of snow phenology with latitude and longitude was mainly related to the topography of Xinjiang. Snow phenological parameters of different land-use types were different. The SCD values in wasteland were the lowest and the SED was the earliest, while forest land was the first to enter SOD accumulation. According to the study, the mean annual values of SCD, SOD, and SED were 25, 342 (8 December), and 51 (8 February) as day of year (DOY), respectively. The snow cover area was mainly distributed in the Altai Mountains, Junggar Basin, Tianshan Mountains, and Kunlun Mountains. 2. The variation trend and significance of snow cover phenological parameters in different regions are different, and the variation trend of snow cover phenological parameters in most regions of Xinjiang is non-significant. [ABSTRACT FROM AUTHOR]

Solar irradiance data include temporal information and geospatial information, so solar irradiance prediction can be regarded as a spatiotemporal sequence prediction problem. However, at present, most of the research is based on time series prediction models, and the research studies on spatial-temporal series prediction models are relatively few. Therefore, it is necessary to integrate spatial-temporal information to construct a spatial-temporal sequence prediction model for research. In this paper, the spatial-temporal prediction model based on graph convolutional network (GCN) and long short-term memory network (LSTM) was established for short-term solar irradiance prediction. In this model, solar radiation observatories were modeled as undirected graphs, where each node corresponds to an observatory, and a GCN was used to capture spatial correlations between sites. For each node, temporal features were extracted by using a LSTM. In order to evaluate the prediction performance of this model, six solar radiation observatories located in the Xinjiang region of China were selected; together with widely used persistence model seasonal autoregressive integrated moving average and data-driven prediction models such as convolutional neural network, recurrent neural network, and LSTM, comparisons were made under different seasons and weather conditions. The experimental results show that the average root mean square error of the GCN-LSTM model at the six sites is 62.058 W / m 2 , which is reduced by 9.8%, 14.3%, 6.9%, and 3.3%, respectively, compared with other models; the average MAE is 25.376 W / m 2 , which is reduced by 27.7%, 26.5%, 20.1%, and 11%, respectively, compared with other models; the average R2 is 0.943, which is improved by 1.4%, 2.2%, 0.8%, and 0.4%, respectively, compared with other models. [ABSTRACT FROM AUTHOR]

In gas drilling operations, the rate of penetration (ROP) parameter has an important influence on drilling costs. Prediction of ROP can optimize the drilling operational parameters and reduce its overall cost. To predict ROP with satisfactory precision, a stacked generalization ensemble model is developed in this paper. Drilling data were collected from a shale gas survey well in Xinjiang, northwestern China. First, Pearson correlation analysis is used for feature selection. Then, a Savitzky-Golay smoothing filter is used to reduce noise in the dataset. In the next stage, we propose a stacked generalization ensemble model that combines six machine learning models: support vector regression (SVR), extremely randomized trees (ET), random forest (RF), gradient boosting machine (GB), light gradient boosting machine (LightGBM) and extreme gradient boosting (XGB). The stacked model generates meta-data from the five models (SVR, ET, RF, GB, LightGBM) to compute ROP predictions using an XGB model. Then, the leave-one-out method is used to verify modeling performance. The performance of the stacked model is better than each single model, with R2 = 0.9568 and root mean square error = 0.4853 m/h achieved on the testing dataset. Hence, the proposed approach will be useful in optimizing gas drilling. Finally, the particle swarm optimization (PSO) algorithm is used to optimize the relevant ROP parameters. [ABSTRACT FROM AUTHOR]

Copyright of Arid Zone Research / Ganhanqu Yanjiu is the property of Arid Zone Research Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Power stations operating on PhotoVoltaic (PV) power generation are challenged by demand forecasting as PV power generation is random and intermittent. This paper presents an Efficient Shrinkage Temporal Convolutional Network (ESTCN) model, which combines the Temporal Convolutional Network (TCN) and an improved Deep Residual Shrinkage Network (DRSN) to forecast PV power output. First, the attention sub-network of the DRSN is improved, the fully connected layer of the sub-network is canceled, and feature extraction is done directly on the model features following global average pooling using a one-dimensional convolution to obtain cross-channel interaction and increased model efficiency. Next, an improved attention mechanism and adaptive soft thresholding are introduced into TCN to automatically determine the noise threshold to address the issue of information weight dispersion caused by redundant information in the input samples. By incorporating dilated causal convolution, attention module, soft thresholding, and residual connection, the ESTCN model is formed and is shown to enhance PV power output prediction with only a minimal increase in the number of parameters. The power station in Ningxia, China is adopted as a validation example with data taken from the year 2020. The fitting and prediction results of the ESTCN model are compared against the Convolutional Neural Network, Long Short-Term Memory, and TCN models. The ESTCN model yielded the following values of the evaluation metrics: Root Mean Square Error (RMSE) of 1.47 kW, Mean Absolute Error (MAE) of 0.79 kW, and coefficient of determination of 0.999. Compared to the TCN model, the prediction errors based on RMSE and MAE improved by 0.39 kW and 0.18 kW, respectively. Applying the ESTCN model to predict PV power generation of power stations in two regions in China, Ningxia and Xinjiang, shows the ESTCN model to be superior, scalable, and universal over other deep learning models. • Study the problem of photovoltaic power using a deep learning method. • A new Efficient Shrinkage Temporal Convolutional Network model is presented. • Attention sub-network of DRSN is improved by cancelling the fully connected layer. • Feature extraction is done on the model features using a one-dimensional convolution. • Improved attention mechanism and adaptive soft thresholding are introduced into TCN. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Irrigation & Drainage is the property of Journal of Irrigation & Drainage Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The optimized agricultural water schedule and management, especially in the large irrigation district along the river basins, were closely related to the spatial and temporal runoff variations. However, the impacts of climate change and human activities leads to non-linear and non-stationary monthly runoffs. Under this condition, effectively capturing the variation of the runoff time series and improving the accuracy of the prediction model are of vital importance. However, some existing monthly data-driven prediction studies mainly focus on the model structure and calculation load, ignoring the influence of each frequency component of runoff and the fluctuation of runoff series caused by meteorological factors, which may not effectively capture the potential change process. In this paper, a novel method (CEEMD-MPE-EMD-GRU) for the non-stationary monthly runoff prediction was proposed. It fully took advantages of the complementary ensemble empirical mode decomposition (CEEMD), multi-scale permutation entropy (MPE), empirical mode decomposition (EMD) and gated recurrent unit (GRU). The combined model in general is a data-driven model, and compared with the traditional mechanism model, its most notable advantage is that it successfully overcomes the redundant information of the prediction model. In addition, atmospheric input factor analysis is added on the basis of fully decomposing and identifying non-stationary pseudo-components. The hydrological runoff data (1956–2014) obtained from the Manas River locating at Xinjiang, China were used for prediction. The results indicated that the new CEEMD-MPE-EMD-GRU model reached higher accuracy, as its Nash-Sutcliffe efficiency coefficient (0.960) was significantly larger than those of the GRU model (0.813) and the CEEMD-GRU model (0.889). Meanwhile, the root mean square error and the absolute relative error of the CEEMD-MPE-EMD-GRU model decreased to 0.279 and 0.195, respectively. The new runoff prediction model established in this paper would provide more precise evaluation of the monthly runoff prediction and better guidelines for high-efficiency agricultural water scheduling in the irrigation district. • Introduced CEEMD-MPE-EMD-GRU to predict the monthly series of streamflow. • Optimize the hydrometeorological elements as the input items of the model. • The CEEMD-MPE-EMD-GRU is found to be stable on tendency and extreme streamflow. • It overcomes a lot of steps brought by redundant information of the model. • Effectively improve the accuracy of nonlinear and non-stationary flow simulation. [ABSTRACT FROM AUTHOR]

Based on North American Multi Model Ensemble (NMME) and monthly average soil moisture return data of five models in Xinjiang region during 1982 -- 2010, using ERA-interim reanalysis soil moisture data for verification, the abilities of five models and simple ensemble mean to predict winter soil moisture in Xinjiang region were evaluated. The results showed that forecasting skill of CanCM4 in single model was the best, while forecasting skill of CESM1 was the lowest. With the increase of lead time, the forecasting skills of all models gradually decreased, containing NMME. But NMME gradually reflects the advantages in longer lead time, and its forecasting skills in Leadl-Season, Lead2-Season and Lead3-Season were higher than all single model, illustrating that multi model simple ensemble mean is more useful for predicting soil moisture in longer lead time, which is helpful for improving prediction skills. In spatial distribution, correlation of all models in winter in southwest Xinjiang (Tarim Basin) and northwest region was worse. CanCM3 and CanC.M4 models had higher correlation in northern Xinjiang. Meanwhile, root mean square error was also smaller in corresponding region and larger in southwest region. Forecasting skills of GFDL-CM2pl and CESM1 in eastern area of central Xinjiang were higher, and root mean square error in this area was also smaller. This paper also could lay the foundation for further studying prediction of soil drought. [ABSTRACT FROM AUTHOR]

To optimize the growth management of spring maize, it is essential to understand the dynamics of plant height and leaf area index (LAI) under controlled water and nitrogen supply. This study conducted two-year field experiments (2022–2023) in Karamay, Xinjiang. Three irrigation levels (75%, 100%, and 125% of Crop Evapotranspiration (ETc)) and four nitrogen application rates (0, 93, 186, and 279 kg N/ha) were set. A logistic growth model was fitted using accumulated effective temperature as the independent variable to analyze the growth and development characteristics of spring maize under various water and nitrogen conditions. The results demonstrated that the logistic models, based on relative effective accumulated temperature, had a determination coefficient (R2) of over 0.99 and a Normalized Root Mean Square Error (NRMSE) of less than 10%. Irrigation extended the rapid growth phase of plant height, whereas nitrogen application shortened the time to enter this rapid growth phase and prolonged its duration. Irrigation increased the maximum LAI growth rate and shortened and prolonged the rapid growth phase, while nitrogen extended the duration of the rapid growth phase for LAI. The W2N2 treatment, consisting of 100% ETc irrigation and 186 kg N/ha, was identified as the optimal drip irrigation water–nitrogen combination for spring maize in the study area. Under optimal water and nitrogen supply, both the maximum growth rate and the average growth rate during the rapid growth phase were higher, requiring accumulated effective temperatures of 825.16–845.74 °C·d and 856.68–890.00 °C·d, respectively, to reach these rates. The appropriate water and nitrogen supply significantly enhanced the synergistic promotion of growth and development in spring maize. This study provides a theoretical basis for the quantitative analysis of growth dynamics in summer maize using effective accumulated temperature. [ABSTRACT FROM AUTHOR]

Drought stress disrupts the molecular-level water balance in plants, and severe water deficiency can be fatal for cotton plants. However, mild water deficits or short-term drought stress may enhance crop resilience, increasing yields. The present study aims to determine the optimal watering time and irrigation amount to induce drought tolerance in cotton seedlings during drought training. Specifically, the investigation focuses on identifying the ideal day for watering and the corresponding irrigation volume that effectively triggers the transition of cotton plants into a state of enhanced resistance to drought stress during the seedling stage. In this study, the CSM-CROPGRO-Cotton model was utilized, and our objectives were to (i) evaluate the predictive capability of CSM-CROPGRO-Cotton for yield estimation in field experiments in Xinjiang and (ii) simulate and assess the range of time during the seedling stage when cotton plants can withstand drought stress without reducing yields, identifying irrigation strategies that induce drought training while maintaining yield under mild water deficiency. The model was validated using yield data from field experiments conducted in 2023. The validation criteria included a normalized root mean square error (n R M S E) > 10 % and a coefficient of determination (r 2) > 85 % for yield; for the leaf area index (LAI), the criterion was (r 2) > 90 % , with a degree of agreement of (d) > 75 % . The results demonstrated the accuracy of the CSM-CROPGRO-Cotton model in predicting cotton yield. Based on the validated CSM-CROPGRO-Cotton model, this study employed the LINUX crop model batch-processing technique to efficiently simulate 357 different irrigation strategies by adjusting the amount of "first irrigation" and timing. The findings revealed that in the irrigation scheme for cotton during the seedling stage, when the amount of first irrigation was in the lower range of 10 mm to 15 mm, the cotton plants underwent drought training during the early growth stage, and their yields did not exhibit drastic fluctuations due to reduced amounts of first irrigation. The suitable period for first irrigation for drought training was from 25 June to 6 July, and the amount of first irrigation could save approximately 57.14% in irrigation water. This implies that subjecting cotton plants to a certain level of drought training can enhance their stress tolerance and increase yields. This finding holds great significance for cotton cultivation in drought-prone regions. [ABSTRACT FROM AUTHOR]

There are many mountain torrent disasters caused by melting icebergs and snow in Xinjiang, which are very different from traditional mountain torrent disasters. Most of the areas affected by snowmelt are in areas without data, making it very difficult to predict and warn of disasters. Taking the Lianggoushan watershed at the southern foot of Boroconu Mountain as the research subject, the key factors were screened by Pearson correlation coefficient and the factor analysis method, and the data of rainfall, water level, temperature, air pressure, wind speed, and snow depth were used as inputs, respectively, with support vector regression (SVR), random forest (RF), k-nearest neighbor (KNN), artificial neural network (ANN), recurrent neural network (RNN), and long short-term memory neural network (LSTM) models used to simulate the daily average water level at the outlet of the watershed. The research results showed that the root mean square error (RMSE) values of SVR, RF, KNN, ANN, RNN, and LSTM in the training period were 0.033, 0.012, 0.016, 0.022, 0.011, and 0.010, respectively, and in the testing period they were 0.075, 0.072, 0.071, 0.075, 0.075, and 0.071, respectively. The performance of LSTM was better than that of other models, but it had more hyperparameters that needed to be optimized. The performance of RF was second only to LSTM; it had only one hyperparameter and was very easy to determine. The RF model showed that the simulation results mainly depended on the average wind speed and average sea level pressure data. The snowmelt model based on machine learning proposed in this study can be widely used in iceberg snowmelt warning and forecasting in ungauged areas, which is of great significance for the improvement of mountain flood prevention work in Xinjiang. [ABSTRACT FROM AUTHOR]

Surface fine particulate matter (PM) with a diameter of less than 2.5 microns (PM2.5) negatively impacts human health and the economy. However, due to data and model limitations, obtaining high-quality, high-spatial-resolution surface PM2.5 concentration data is a challenging task, and it is difficult to accurately assess the temporal and spatial changes in PM2.5 levels at a small regional scale. Here, we combined multi-angle implementation of atmospheric correction (MAIAC) aerosol products, ERA5 reanalysis data, etc., to construct an STW-LightGBM model that considers the spatiotemporal characteristics of air pollution and estimate the PM2.5 concentration of China's surface at 1 km resolution from 2015 to 2020. Our model performed well, and the fitting accuracy of the 10-fold cross-validation between years was 0.877–0.917. The fitting accuracy of the model was >0.85 at different time scales (month, season, and year). The average slope of the regression prediction was 0.9 annually. The results showed that PM2.5 pollution improved from 2015 to 2020. The average PM2.5 concentration decreased by 4.55 μg/m3, and the maximum decrease reached 90.51 μg/m3. The areas with high PM2.5 concentrations were predominantly in the North China Plain, Sichuan Basin, and Xinjiang in the west, and the levels in areas with elevated PM2.5 levels were consistent across most study years. The standard deviation ellipse for PM2.5 in China showed a 'northeast–southwest' spatial distribution. From an interannual perspective, the average values of the four seasonal stations in the country showed a downward trend from 2015 to 2020, with the most obvious decline in winter, from 70.67 μg/m3 in 2015 to 46.75 μg/m3 in 2020. Compared to earlier inversion studies, this work provides a more stable and accurate method for obtaining high-resolution PM2.5 data, which is necessary for local air governance and environmental ecological construction at a fine scale. [ABSTRACT FROM AUTHOR]

This paper established hybrid prediction models based on variational mode decomposition (VMD), empirical mode decomposition (EMD), and ensemble empirical mode decomposition (EEMD) combined with the backpropagation neural network model (BPNN) to improve the prediction accuracy of reference crop evapotranspiration (ET 0) time series with the characteristics of nonlinearity and instability. The daily ET 0 data of 11 representative stations in Xinjiang from 1993 to 2016 were selected for model training and testing and compared with the results of support vector regression (SVR) and gradient boosting regression tree (GBRT) as the two empirical machine learning models. The results indicated the superiority of the VMD-BPNN hybrid prediction model to EMD-BPNN and EEMD-BPNN in terms of accuracy and stability, with root mean square error (RMSE) = 0.405 mm/d, mean absolute error (MAE) = 0.268 mm/d, and coefficient of determination (R2) = 0.979. When employing the VMD-BPNN model to forecast ET 0 for seven days, the RMSE and Nash-Sutcliffe efficiency coefficient (NSE) of the VMD-BPNN model were 0.588 mm/d and 0.952, respectively, and the prediction results indicated high precision and reliability. The prediction accuracy of the VMD-BPNN model was significantly higher than that of single machine learning models, such as BPNN, SVR, and GBRT. The RMSE and MAE values of the VMD-BPNN model were more than 60% smaller than BPNN, SVR, and GBRT models, and R2 and NSE were approximately 18% higher than BPNN, SVR, and GBRT models, respectively. This demonstrates the effectiveness of the VMD method in reducing the non-stationarity of the original daily ET 0 data. The BPNN model predicted the decomposed data series, and the prediction accuracy and stability were significantly enhanced. This indicates the high reliability of the VMD-BPNN model and its capability for ET 0 prediction in Xinjiang. • The VMD-BPNN model is the best among three hybrid models in predicting daily ET 0. • The VMD-BPNN model performs good on sites with different spatial distributions. • The VMD-BPNN model had higher accuracy in predicting 7-day ET 0. • Hybrid prediction model has higher accuracy than the BPNN, SVR, GBRT model. [ABSTRACT FROM AUTHOR]

It is crucial but challenging to detect intermediate or end products promptly. Traditional chemical detection methods are time-consuming and cannot detect mineral phase content. Thermal infrared hyperspectral (TIH) technology is an effective means of real-time imaging and can precisely capture the emissivity characteristics of objects. This study introduces TIH to estimate the content of potassium salts, with a model based on Competitive Adaptive Reweighted Sampling (CARS) and Partial Least Squares Regression (PLSR). The model takes the emissivity spectrum of potassium salt into account and accurately predicts the content of Mixing Potassium (MP), a mineral mixture produced in Lop Nur, Xinjiang. The main mineral content in MP was measured by Mineral Liberation Analyzer (MLA), mainly including picromerite, potassium chloride, magnesium sulfate, and less sodium chloride. 129 configured MP samples were divided into calibration (97 samples) and prediction (32 samples) sets. The CARS-PLSR method achieved good prediction results for MP mineral content (picromerite: correlation coefficient of correction set ( R p 2 ) = 0.943, predicted root mean square error (RMSEP) = 2.72%, relative predictive deviation (RPD) = 4.24; potassium chloride: R p 2 = 0.948, RMSEP = 2.86%, RPD = 4.42). Experimental results convey that TIH technology can effectively identify the emissivity characteristics of MP minerals, facilitating quantitative detection of MP mineral content. [ABSTRACT FROM AUTHOR]

Grassland biomass monitoring is essential for assessing grassland health and carbon cycling. However, monitoring grassland biomass in drylands based on satellite remote sensing is challenging. Statistical regression models and machine learning have been used for the construction of grassland biomass models, but the predictive power for different grassland types is unclear. Additionally, the selection of the most appropriate variables to construct a biomass inversion model for different grassland types must be explored. Therefore, 1201 groundtruthed data points collected from 2014-2021, including 15 Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices, geographic location and topographic data, and meteorological factors and vegetation biophysical indicators were screened for key variables using principal component analysis (PCA). The accuracy of multiple linear regression models, exponential regression models, power function models, support vector machine (SVM) models, random forest (RF) models, and neural network models was evaluated for the inversion of three types of grassland biomass. The results were as follows: (1) The biomass inversion accuracy of single vegetation indices was low, and the optimal vegetation indices were the soil-adjusted vegetation index (SAVI) (R2 = 0.255), normalized difference vegetation index (NDVI) (R2 = 0.372) and optimized soiladjusted vegetation index (OSAVI) (R2 = 0.285). (2)Grassland above-ground biomass (AGB) was affected by various factors such as geographic location, topography, and meteorological factors, and the inverse models using a single environmental variable had large errors. (3) The main variables used to model biomass in the three types of grasslands were different. SAVI, aspect, slope, and precipitation (Prec.) were selected for desert grasslands; NDVI, shortwave infrared 2 (SWI2), longitude, mean temperature, and annual precipitation were selected for steppe;and OSAVI, phytochrome ratio (PPR), longitude, precipitation, and temperature were selected for meadows. (4) The nonparametric meadow biomass model was superior to the statistical regression model. (5) The RF model was the best model for the inversion of grassland biomass in Xinjiang, and this model had the highest accuracy for grassland biomass inversion (R² = 0.656, root mean square error (RMSE) = 815.6 kg/ha), followed by meadow (R² = 0.610, RMSE = 547.9 kg/ha) and desert grassland (R2 = 0.441, RMSE = 353.6 kg/ha). [ABSTRACT FROM AUTHOR]

The land surface scheme (LSS) plays a very important role in the forecast of surface meteorological elements in the Central Asia arid region. Therefore, two LSSs viz. Noah and Noah-MP were evaluated over the Central Asia arid region in January and July 2017 using the Weather Research and Forecasting (WRF) model at a 3-km horizontal grid resolution. The objective was to assess the performance of WRF LSSs by calculated the mean error (ME) and the root mean squared error (RMSE) of simulated hourly meteorological elements, such as 2-m air temperature, 10-m wind speed, soil temperature, soil moisture at 5 and 25 cm thickness, and surface soil heat flux at 5 cm thickness. The results showed that, compared to Noah, Noah-MP modeled less surface sensible heat flux in the northern Xinjiang (15~20 W∙m−2) and surface latent heat flux in most areas of Xinjiang (<10 W∙m−2) in January, and mainly generated less sensible heat flux in most areas of north Xinjiang and the mountainous regions of southern Xinjiang (≤20 W·m−2) which on the contrary, generated more surface latent heat flux in most parts of Xinjiang (15~20 W·m−2) in July. Meanwhile, the surface soil heat flux generated from Noah-MP was closer to the observations at Hongliuhe and Kelameili stations in January, the ME increased by 17.5% and reduced by 80.7%, respectively, the RMSE decreased by 44.4% and 61.7%, respectively, and closer to the observations at Xiaotang station in July, the ME and RMSE reduced by 19.1% and 20.5%, respectively. Compared to Noah, Noah-MP improved the overall simulation of soil temperature and soil moisture over the northern and eastern Xinjiang (at 10 cm thickness), the ME and RMSE of simulated soil temperature reduced by 85.0% and 13.4% in January, decreased by 78.6% and increased by 6.2% in July, respectively, and the ME and RMSE of simulated soil moisture reduced by 67.2% and 14.9% in January, reduced by 33.3% and 2.8% in July, respectively. Compared to Noah, Noah-MP's results were lowered for the simulated 10-m wind speed and 2-m air temperature, especially the simulated 2-m air temperature over the cold climate regions of northern Xinjiang, was improved significantly, the ME and RMSE of simulated 10-m wind speed reduced by 0.8% and 4.9% in January, decreased by 6.7% and 2.8% in July, respectively, the ME and RMSE of simulated 2-m air temperature reduced by 2.8% and 1.0% in July, respectively. This study demonstrated the advantage of coupled Noah-MP over the Central Asia arid region, providing the basis for WRF/Noah-MP in future operational applications in the Central Asia arid region. [ABSTRACT FROM AUTHOR]

Soil salinization endangers the development of ecological agriculture. As soil salinization is often heavily affected by regional environments, difficulties arise when constructing an adaptive multi-regional soil salinity estimation model. In this study, we proposed an extreme gradient boosting (XGBoost) model based on the Tree-structure Parzen Estimator (TPE) optimization algorithm to apply to four study areas with different environments (TPE-XGBoost). The four areas are the Weigan-Kuqa Oasis (Weiku), the Sangong River Basin (Sgr) and the Qitai Oasis in Xinjiang, China, and the middle and lower reaches of the Syr Darya Basin in Kazakhstan. Most previous soil salinity studies did not pay much attention to the impact of feature selection and hyper-parameter tuning on the performance of machine learning models, and the complex dependence and interaction between input features and hyper-parameters. In order to improve the performance of XGBoost model in estimating soil salinity, we proposed for the first time to use TPE algorithm to jointly optimize feature selection and hyper-parameter tuning, and verified it in four areas. Coefficient of determination (R2) and Root Mean Square Error (RMSE) were used to evaluate the model performance. First, we calculated 55 environmental features from Landsat and terrain data. Then, in order to reduce the computational complexity of the TPE-XGBoost model, we used Pearson correlation analysis between surface soil salinity content (SSC) and features to initially filter out the features that were not significantly related (P > 0.05). Finally, the TPE algorithm was used to jointly optimize the parameter space composed of features and hyper-parameters. The results showed that (1) TPE joint optimization algorithm significantly improved the performance of the XGBoost model, achieving high accuracy in the four areas, and had powerful generalization. R2 values of test sets for Weiku Oasis, Qitai Oasis, Sgr Basin, and the Syr Basin were 0.95, 0.95, 0.80, and 0.81, respectively. (2) There is no universal feature can be applied to soil salinity inversion in different environments. TPE algorithm adaptively selected different types and numbers of features for four areas, 19, 11, 25, and 15 features were selected in Weiku Oasis, Qitai Oasis, Sgr Basin, and the Syr Basin, respectively. This showed that the optimal model parameters should not be fixed parameters, but should be re-determined locally according to different environmental conditions. The TPE algorithm can capture the features that reflect environmental differences. (3) The XGBoost model can provide feature importance ranking, which improves the interpretability of machine learning model. The importance analysis results showed that the features had different contributions in different areas. The TPE-XGBoost model proposed in this study has great potential in multi-regional soil salt estimation research. [ABSTRACT FROM AUTHOR]

Though the use of reanalysis datasets to analyze snow changes is increasingly popular, the snow depth variability in China simulated by multiple reanalysis datasets has not been well evaluated. Also, the extent of regional snow depth variability and its driving mechanisms are still unknown. In this study, monthly snow depth observations from 325 stations during the period of 1981–2018 were taken to evaluate the ability of five reanalysis datasets (JRA-55, MERRA-2, GLDAS2, ERA5, and ERA5L) to simulate the spatial and temporal variability of snow depth in China. The evaluation results indicate that MERRA-2 has the lowest root-mean-square deviation of snow depth and a high spatial correlation coefficient with observations. This may be partly related to the high accuracy of precipitation and temperature in MERRA-2. Also, the 31 combinations of the five reanalysis datasets do not yield better accuracy in snow depth than MERRA-2 alone. This is because the other four datasets have larger uncertainty. Based on MERRA-2, four hot-spot regions with significant snow depth changes from 1981 to 2018 were identified, including the central Xinjiang (XJ-C), the southern part of the northeastern plain and mountain (NPM-S), and the southwestern (TP-SW) and southeastern (TP-SE) portions of the Tibetan Plateau. Snow depth changes mostly occurred in spring in TP-SW and winter in XJ-C, NPM-S, and TP-SE. The snow depth increase in XJ-C, NPM-S, and TP-SW is mainly caused by increased seasonal precipitation, while the snow depth decrease in TP-SE is attributed to the combined effects of decreased precipitation and warming temperature in winter. [ABSTRACT FROM AUTHOR]

Accurate assessment of the spatial distribution and severity of soil salinization has long plagued local governments and researchers in the arid parts of Xinjiang Uygur Autonomous Region (XJUAR). The emergence of machine learning has brought hope to this research field, such as Random Forest (RF) and Stochastic Gradient Treeboost (SGT),however, which are few applications to the quantitative assessment of soil salinization. Therefore, in order to evaluate the accuracy level of the two algorithms for predicting soil salinity, twenty-seven environmental subsets were designed. Each data set is calculated using both RF and SGT to produce an optimal set of variables. The simulation results from 70.37% (19/27) of the subsets showed that the predicted value of soil salinity from SGT is closer to the observed value than is that from RF. The statistics of all datasets showed that the average values of R2 value for RF and SGT were 0.38 and 0.40, the average Root Mean Squared Error (RMSE) value were 28.59 and 27.46, and the Ratio of Prediction to Deviation (RPD) averages were 1.20 and 1.24, respectively. The important dominant factor were topographic variables with coarse resolution, temperature and vegetation indices, land use and landform. [ABSTRACT FROM AUTHOR]

With the continuous development of geochemical mapping, especially with the proposal of the "Chemical Earth" big science project, low-density geochemical mapping technology has received increasing attention due to "low cost and high efficiency". Due to the special geochemical properties of Au, the distribution of Au in water sediments is so complicated that the differences of low-density and high-density Au geochemical mapping are not well known, which makes it difficult for researchers to accurately evaluate the Au anomalies from low-density geochemical mapping. This paper compares Au geochemical mapping data and geochemical patterns from ultra-low-density geochemical mapping (ULGM) (1 sample/1600 km2), super-low-density geochemical mapping (SLGM) (1 sample/180 km2) and high-density geochemical mapping (HGM) (sampling density 1 sample/km2) in Altay, Xinjiang province, China. We found that the standard deviation of Au increases with the mapping density. The median values of the HGM, SLGM and ULGM are 0.71 × 10−9, 0.77 × 10−9, and 0.94 × 10−9, respectively. Altay Au anomalous is mainly controlled by 3 factors and are consequently divided into 3 kinds of anomaly types: 1. regional-scale anomalies, caused by gold-rich stratum; 2. local Au anomalies, caused by gold mineralization; 3. anomalies around the catchment, caused by the supergene migration and enrichment of fine-grained Au. In general, as the mapping density decreases, the spatial correspondence between anomalies and geological factors become weaker. HGM can figure out the single gold deposit, SLGM can delineate Au deposits concentrated area; ULGM can delineate the Au metallogenic target area. So, the ultra-low density mapping is suitable for quickly obtaining the geochemical background of the Au region in the blank area, reflecting the large-scale enrichment behavior of Au in the supergene environment. • Au geochemical mapping projects in three kinds of density are compared for the first time. • High-density geochemical mapping can be used to figure out the single gold deposit. • Super-low-density geochemical mapping can delineate Au deposits concentrated area. • Ultra-low-density geochemical mapping can delineate the Au metallogenic target area. [ABSTRACT FROM AUTHOR]

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The measurement of snow depth based on temperature brightness with passive microwave sensing is still achallenging problem. Snow depth for the snow cover hydrological model and climate model is asignificant input parameter. Hence, this study concentrates on Inversion Model Assisted Vector Analysis (IMAV) for estimating snow depth in north Xinjiang based on the brightness of temperature. Further, the estimated set of IMAV has been hybridized to address the problem. The results suggested that for both horizontal and vertically polarized PMW radiation the IMAV outperforms SVM at 11.05, 19.6, and 38.4 GHz. If the root mean square error (RMSE) in the IMAV performance is 8 K or below, compared with anormal SVM calculation, then the average over the nine-year study period across the North Xinjiang region of China, the failure correlation coefficient is 7 or greater. Compared with SVM tests, the RMSE was decreased by more than 17% for any of the six frequencies and polarization combinations evaluated, while the anomaly coefficient was raised by more than 50%.Such results suggest that the IMAV is asuperior alternative to the SVM for subsequent use in adata assimilation system as acalculating operator. [ABSTRACT FROM AUTHOR]

Common Mode Error (CME) presents a kind of spatially correlated error that is widespread in regional Global Navigation Satellite System (GNSS) networks and should be eliminated during postprocessing of a GNSS position time series. Several spatiotemporal filtering methods have been developed to mitigate the effects of CME. However, such methodologies become inappropriate when missing and noisy data exists. In this research, we introduce a novel spatial filtering algorithm called Weighted Expectation Maximization Principal Component Analysis (WEMPCA) for detecting and removing CME from noisy GNSS position time series with missing values, among which formal errors of daily GNSS solutions are utilized to weight the input data. Compared with traditional PCA and the special case of EMPCA, simulation experiments demonstrate that the new WEMPCA algorithm always has outstanding performance over others. The WEMPCA algorithm was then successfully used to extract the CME from real noisy and missing GNSS position time series in Xinjiang province. Our results show that only the first principal component exhibits significant spatial response, with average values of 70.11%, 66.53%, and 52.45% for North, East, and Up (NEU) components, respectively, indicating that it represents the CME of this region. After removing CME, the canonical correlation coefficients and root mean square error of GNSS residual time series, as well as the amplitudes of power-law noises (PLN), are obviously decreased in all three directions. However, the white noise (WN) amplitudes are found to diminish exclusively in the North and East component, not in the Up components. Moreover, the average velocity differences before and after filtering CME are 0.19 mm/year, 0.03 mm/year, and −0.56 mm/year for the NEU components, respectively, indicating that CME has an influence on the GNSS station velocity estimation. The velocity uncertainty is also reduced by 43.51%, 38.64%, and 40.39% on average for the NEU components, respectively, implying that the velocity estimates are more reliable and accurate after removing CME. Therefore, we conclude that the new WEMPCA approach provides an efficient solution to detect and mitigate CME from the noisy and missing GNSS position time series. [ABSTRACT FROM AUTHOR]