Accurate lake depth mapping and estimation of changes in water level and water storage are fundamental significance for understanding the lake water resources on the Tibetan Plateau.In this study,combined with satelli...Accurate lake depth mapping and estimation of changes in water level and water storage are fundamental significance for understanding the lake water resources on the Tibetan Plateau.In this study,combined with satellite images and bathymetric data,we comprehensively evaluate the accuracy of a multi-factor combined linear regression model(MLR)and machine learning models,create a depth distribution map and compare it with the spatial interpolation,and estimate the change of water level and water storage based on the inverted depth.The results indicated that the precision of the random forest(RF)was the highest with a coefficient of determination(R2)value(0.9311)and mean absolute error(MAE)values(1.13 m)in the test dataset and had high reliability in the overall depth distribution.The water level increased by 9.36 m at a rate of 0.47 m/y,and the water storage increased by 1.811 km3 from 1998 to 2018 based on inversion depth.The water level change was consistent with that of the Shuttle Radar Topography Mission(SRTM)method.Our work shows that this method may be employed to study the water depth distribution and its changes by combining with bathymetric data and satellite imagery in shallow lakes.展开更多
We consider a discrete time Storage Process Xn with a simple random walk input Sn and a random release rule given by a family {Ux, x ≥ 0} of random variables whose probability laws {Ux, x ≥ 0} form a convolution sem...We consider a discrete time Storage Process Xn with a simple random walk input Sn and a random release rule given by a family {Ux, x ≥ 0} of random variables whose probability laws {Ux, x ≥ 0} form a convolution semigroup of measures, that is, μx × μy = μx + y The process Xn obeys the equation: X0 = 0, U0 = 0, Xn = Sn - USn, n ≥ 1. Under mild assumptions, we prove that the processes and are simple random walks and derive a SLLN and a CLT for each of them.展开更多
基金supported by 2020 Science and technology project of innovation ecosystem construction,National Supercomputing Zhengzhou center-Research on Key Technologies of intelligent fine prediction based on big data analysis[grant number:201400210800]Second Tibetan Plateau Scientific Expedition and Research(STEP)[grant number:2019QZKK0202]+3 种基金the CAS Alliance of Field Observation Stations[grant number:KFJ-SW-YW038]CAS Strategic Priority Research Program:[Grant Number XDA19020303,XDA20020100]the Ministry of Science and Technology of China Project[grant number:2018YFB05050000]National Natural Science Foundation of China project[grant number:41831177,41901078].
文摘Accurate lake depth mapping and estimation of changes in water level and water storage are fundamental significance for understanding the lake water resources on the Tibetan Plateau.In this study,combined with satellite images and bathymetric data,we comprehensively evaluate the accuracy of a multi-factor combined linear regression model(MLR)and machine learning models,create a depth distribution map and compare it with the spatial interpolation,and estimate the change of water level and water storage based on the inverted depth.The results indicated that the precision of the random forest(RF)was the highest with a coefficient of determination(R2)value(0.9311)and mean absolute error(MAE)values(1.13 m)in the test dataset and had high reliability in the overall depth distribution.The water level increased by 9.36 m at a rate of 0.47 m/y,and the water storage increased by 1.811 km3 from 1998 to 2018 based on inversion depth.The water level change was consistent with that of the Shuttle Radar Topography Mission(SRTM)method.Our work shows that this method may be employed to study the water depth distribution and its changes by combining with bathymetric data and satellite imagery in shallow lakes.
文摘We consider a discrete time Storage Process Xn with a simple random walk input Sn and a random release rule given by a family {Ux, x ≥ 0} of random variables whose probability laws {Ux, x ≥ 0} form a convolution semigroup of measures, that is, μx × μy = μx + y The process Xn obeys the equation: X0 = 0, U0 = 0, Xn = Sn - USn, n ≥ 1. Under mild assumptions, we prove that the processes and are simple random walks and derive a SLLN and a CLT for each of them.
