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  • Random forest-based prediction of decay modes and half-lives of superheavy nuclei

    分类: 物理学 >> 核物理学 提交时间: 2023-10-12

    摘要: How nuclides decay in the superheavy region is key information for investigating new elements beyond oganesson and the island of stability. The Random Forest algorithm is applied to study the competition between different decay modes in the superheavy region, includingdecay,decay,+decay, electron capture and spontaneous fission. The observed half-lives and dominant decay mode are well reproduced. The dominant decay mode of 96.9 % nuclei beyond212Po is correctly described.decay is predicted to be the dominant decay mode for isotopes in new elementsZ= 119122, except for spontaneous fission in some even-even ones because of the increased Coulomb repulsion and odd-even effect. The predicted half-lives show the existence of a long-lived spontaneous fission island at the southwest of298Fl caused by the competition of nuclear deformation and Coulomb repulsion. More understanding of spontaneous fission, especially beyond286Fl, is crucial to search for new elements and the island of stability.

  • Forecasting solar still performance from conventional weather data variation by machine learning method

    分类: 能源科学 >> 能源(综合) 提交时间: 2022-05-30

    摘要: Solar stills are considered an effective method to solve the scarcity of drinkable water. However, it is still missing a way to forecast its production. Herein, it is proposed that a convenient forecasting model which just needs to input the conventional weather forecasting data. The model is established by using machine learning methods of random forest and optimized by Bayesian algorithm. The required data to train the model is obtained from daily measurements lasting 9 months. To validate the accuracy model, the determination coefficients of two types of solar stills are calculated as 0.935 and 0.929, respectively, which are much higher than the value of both multiple linear regression (0.767) and the traditional models (0.829 and 0.847). Moreover, by appling the model, it is predicted that the freshwater production of four cities in China. The predicted production is approved to be reliable by a high value of correlation (0.868) between the predicted production and the solar insolation. With the help of the forecasting model, it would greatly promote the global application of solar stills.

  • Estimation of soil organic matter in the Ogan-Kuqa River Oasis, Northwest China, based on visible and near-infrared spectroscopy and machine learning

    分类: 地球科学 >> 地理学 提交时间: 2023-02-15 合作期刊: 《干旱区科学》

    摘要: Visible and near-infrared (vis-NIR) spectroscopy technique allows for fast and efficient determination of soil organic matter (SOM). However, a prior requirement for the vis-NIR spectroscopy technique to predict SOM is the effective removal of redundant information. Therefore, this study aims to select three wavelength selection strategies for obtaining the spectral response characteristics of SOM. The SOM content and spectral information of 110 soil samples from the Ogan-Kuqa River Oasis were measured under laboratory conditions in July 2017. Pearson correlation analysis was introduced to preselect spectral wavelengths from the preprocessed spectra that passed the 0.01 level significance test. The successive projection algorithm (SPA), competitive adaptive reweighted sampling (CARS), and Boruta algorithm were used to detect the optimal variables from the preselected wavelengths. Finally, partial least squares regression (PLSR) and random forest (RF) models combined with the optimal wavelengths were applied to develop a quantitative estimation model of the SOM content. The results demonstrate that the optimal variables selected were mainly located near the range of spectral absorption features (i.e., 1400.0, 1900.0, and 2200.0 nm), and the CARS and Boruta algorithm also selected a few visible wavelengths located in the range of 480.0510.0 nm. Both models can achieve a more satisfactory prediction of the SOM content, and the RF model had better accuracy than the PLSR model. The SOM content prediction model established by Boruta algorithm combined with the RF model performed best with 23 variables and the model achieved the coefficient of determination (R2) of 0.78 and the residual prediction deviation (RPD) of 2.38. The Boruta algorithm effectively removed redundant information and optimized the optimal wavelengths to improve the prediction accuracy of the estimated SOM content. Therefore, combining vis-NIR spectroscopy with machine learning to estimate SOM content is an important method to improve the accuracy of SOM prediction in arid land.

  • Environmental factors influencing snowfall and snowfall prediction in the Tianshan Mountains, Northwest China

    分类: 地球科学 >> 地理学 提交时间: 2019-01-17 合作期刊: 《干旱区科学》

    摘要: Snowfall is one of the dominant water resources in the mountainous regions and is closely related to the development of the local ecosystem and economy. Snowfall predication plays a critical role in understanding hydrological processes and forecasting natural disasters in the Tianshan Mountains, where meteorological stations are limited. Based on climatic, geographical and topographic variables at 27 meteorological stations during the cold season (October to April) from 1980 to 2015 in the Tianshan Mountains located in Xinjiang of Northwest China, we explored the potential influence of these variables on snowfall and predicted snowfall using two methods: multiple linear regression (MLR) model (a conventional measuring method) and random forest (RF) model (a non-parametric and non-linear machine learning algorithm). We identified the primary influencing factors of snowfall by ranking the importance of eight selected predictor variables based on the relative contribution of each variable in the two models. Model simulations were compared using different performance indices and the results showed that the RF model performed better than the MLR model, with a much higher R2 value (R2=0.74; R2, coefficient of determination) and a lower bias error (RSR=0.51; RSR, the ratio of root mean square error to standard deviation of observed dataset). This indicates that the non-linear trend is more applicable for explaining the relationship between the selected predictor variables and snowfall. Relative humidity, temperature and longitude were identified as three of the most important variables influencing snowfall and snowfall prediction in both models, while elevation, aspect and latitude were of secondary importance, followed by slope and wind speed. These results will be beneficial to understand hydrological modeling and improve management and prediction of water resources in the Tianshan Mountains.

  • Modelling the dead fuel moisture content in a grassland of Ergun City, China

    分类: 生物学 >> 生态学 提交时间: 2023-06-13 合作期刊: 《干旱区科学》

    摘要:The dead fuel moisture content (DFMC) is the key driver leading to fire occurrence. Accurately estimating the DFMC could help identify locations facing fire risks, prioritise areas for fire monitoring, and facilitate timely deployment of fire-suppression resources. In this study, the DFMC and environmental variables, including air temperature, relative humidity, wind speed, solar radiation, rainfall, atmospheric pressure, soil temperature, and soil humidity, were simultaneously measured in a grassland of Ergun City, Inner Mongolia Autonomous Region of China in 2021. We chose three regression models, i.e., random forest (RF) model, extreme gradient boosting (XGB) model, and boosted regression tree (BRT) model, to model the seasonal DFMC according to the data collected. To ensure accuracy, we added time-lag variables of 3 d to the models. The results showed that the RF model had the best fitting effect with an R2 value of 0.847 and a prediction accuracy with a mean absolute error score of 4.764% among the three models. The accuracies of the models in spring and autumn were higher than those in the other two seasons. In addition, different seasons had different key influencing factors, and the degree of influence of these factors on the DFMC changed with time lags. Moreover, time-lag variables within 44 h clearly improved the fitting effect and prediction accuracy, indicating that environmental conditions within approximately 48 h greatly influence the DFMC. This study highlights the importance of considering 48 h time-lagged variables when predicting the DFMC of grassland fuels and mapping grassland fire risks based on the DFMC to help locate high-priority areas for grassland fire monitoring and prevention.

  • Land use and cover change and influencing factor analysis in the Shiyang River Basin, China

    分类: 地球科学 >> 地理学 提交时间: 2024-02-21 合作期刊: 《干旱区科学》

    摘要: Land use and cover change (LUCC) is the most direct manifestation of the interaction between anthropological activities and the natural environment on Earth's surface, with significant impacts on the environment and social economy. Rapid economic development and climate change have resulted in significant changes in land use and cover. The Shiyang River Basin, located in the eastern part of the Hexi Corridor in China, has undergone significant climate change and LUCC over the past few decades. In this study, we used the random forest classification to obtain the land use and cover datasets of the Shiyang River Basin in 1991, 1995, 2000, 2005, 2010, 2015, and 2020 based on Landsat images. We validated the land use and cover data in 2015 from the random forest classification results (this study), the high-resolution dataset of annual global land cover from 2000 to 2015 (AGLC-2000-2015), the global 30 m land cover classification with a fine classification system (GLC_FCS30), and the first Landsat-derived annual China Land Cover Dataset (CLCD) against ground-truth classification results to evaluate the accuracy of the classification results in this study. Furthermore, we explored and compared the spatiotemporal patterns of LUCC in the upper, middle, and lower reaches of the Shiyang River Basin over the past 30 years, and employed the random forest importance ranking method to analyze the influencing factors of LUCC based on natural (evapotranspiration, precipitation, temperature, and surface soil moisture) and anthropogenic (nighttime light, gross domestic product (GDP), and population) factors. The results indicated that the random forest classification results for land use and cover in the Shiyang River Basin in 2015 outperformed the AGLC-2000-2015, GLC_FCS30, and CLCD datasets in both overall and partial validations. Moreover, the classification results in this study exhibited a high level of agreement with the ground truth features. From 1991 to 2020, the area of bare land exhibited a decreasing trend, with changes primarily occurring in the middle and lower reaches of the basin. The area of grassland initially decreased and then increased, with changes occurring mainly in the upper and middle reaches of the basin. In contrast, the area of cropland initially increased and then decreased, with changes occurring in the middle and lower reaches. The LUCC was influenced by both natural and anthropogenic factors. Climatic factors and population contributed significantly to LUCC, and the importance values of evapotranspiration, precipitation, temperature, and population were 22.12%, 32.41%, 21.89%, and 19.65%, respectively. Moreover, policy interventions also played an important role. Land use and cover in the Shiyang River Basin exhibited fluctuating changes over the past 30 years, with the ecological environment improving in the last 10 years. This suggests that governance efforts in the study area have had some effects, and the government can continue to move in this direction in the future. The findings can provide crucial insights for related research and regional sustainable development in the Shiyang River Basin and other similar arid and semi-arid areas.