Temporal dynamics of turbidity in China' s lakes and reservoirs: Distinct trajectories and corresponding driving mechanism (Necessary data and codes)

Description

In this database, we uploaded the dataset and turbidity inversion code used for a research article entitled: Temporal dynamics of turbidity in China's lakes and reservoirs: Distinct trajectories and corresponding driving mechanisms, which includes: Modeling datasets (Train_ data_for_modeling.xlsx, Test_data_for_modeling.xlsx, Validation_data_for_modeling.xlsx), the 2003-2024 year-by-year turbidity inversion results of the XGBoost model (based on the median of the year), named XGB_MODIS_Water_Yearly_turbidity_full_TIFF_ from2003to2024.zip, Shapfile_Lakes_reservoirs_boundary_in_China_area_above_10_km2.zip) and point shpfile dataset (Shpfile_Lakes_and_Reservoirs_Points.zip), The results of the evolution trend of lakes and reservoirs and the collection dataset of eight types of drivers (Turbidity_yearly_timeseries_from_2003to2024_by_XGBoost_after_data_cleaning.xlsx), in addition to the Python code (based on XGBoost, random forest and KNN turbidity inversion models, named XGBoost_Code.py, Random_Forest_Code.py andKNN_Code.py). Finally, we upload the driving analysis R language code using Bayesian additive regression tree (BART) model combined with conditional permutation importance (CPI) and partial dependence plots (PDP), named BART_Model_with_CPI_and_PDP. R, we hope that the above data and code can promote academic exchange and progress.

Institutions

• Xiamen University

  Fujian, Xiamen
• Loughborough University

  England, Loughborough
• Institute of Geographic Sciences and Natural Resources Research

  Beijing, Beijing
• Beijing Normal University

  Beijing, Beijing
• Peking University

  Beijing, Beijing

Categories

Licence