Dynamic Streamflow Simulation via Online Gradient-Boosted Regression Tree

AbstractStreamflow simulation is of great importance for water engineering design and water resource management. Most existing models simulate streamflow by establishing a quantitative relationship...

[1]  Vijay P. Singh,et al.  The NWS River Forecast System - catchment modeling. , 1995 .

[2]  T. McVicar,et al.  Isolating the impacts of climate change and land use change on decadal streamflow variation: Assessing three complementary approaches , 2013 .

[3]  Stelios Psarakis,et al.  Multivariate statistical process control charts: an overview , 2007, Qual. Reliab. Eng. Int..

[4]  Mario Chica-Olmo,et al.  An assessment of the effectiveness of a random forest classifier for land-cover classification , 2012 .

[5]  Andy Liaw,et al.  Classification and Regression by randomForest , 2007 .

[6]  Ricard Gavaldà,et al.  Learning from Time-Changing Data with Adaptive Windowing , 2007, SDM.

[7]  Kung-Sik Chan,et al.  Quantifying the effect of land use land cover change on increasing discharge in the Upper Mississippi River , 2010 .

[8]  Soroosh Sorooshian,et al.  Simulating California reservoir operation using the classification and regression‐tree algorithm combined with a shuffled cross‐validation scheme , 2015 .

[9]  O. Kisi.  Wavelet regression model for short-term streamflow forecasting. , 2010 .

[10]  Tiantian Yang,et al.  Non‐stationary bias correction of monthly CMIP5 temperature projections over China using a residual‐based bagging tree model , 2017 .

[11]  Chia-Shang James Chu,et al.  Time Series Segmentation: A Sliding Window Approach , 1995, Inf. Sci..

[12]  Chong-yu Xu,et al.  Hydrological Modeling in a Changing Environment: Issues and Challenges * , 2013 .

[13]  James Theiler,et al.  Accurate On-line Support Vector Regression , 2003, Neural Computation.

[14]  Mac McKee,et al.  Multi-time scale stream flow predictions: The support vector machines approach , 2006 .

[15]  D. Seo,et al.  Overall distributed model intercomparison project results , 2004 .

[16]  Zhenghui Xie,et al.  Regional Parameter Estimation of the VIC Land Surface Model: Methodology and Application to River Basins in China , 2007 .

[17]  Tianqi Chen,et al.  XGBoost: A Scalable Tree Boosting System , 2016, KDD.

[18]  Dawei Han,et al.  Attribution Analysis for Runoff Change on Multiple Scales in a Humid Subtropical Basin Dominated by Forest, East China , 2019, Forests.

[19]  Xianhong Xie,et al.  Tracing Temporal Changes of Model Parameters in Rainfall-Runoff Modeling via a Real-Time Data Assimilation , 2016 .

[20]  Zengxiang Zhang,et al.  Spatial patterns and driving forces of land use change in China during the early 21st century , 2010 .

[21]  J. Friedman.  Greedy function approximation: A gradient boosting machine. , 2001 .

[22]  Dawen Yang,et al.  Impact of climate variability and human activity on streamflow decrease in the Miyun Reservoir catchment , 2010 .

[23]  T. Sakamoto,et al.  A crop phenology detection method using time-series MODIS data , 2005 .

[24]  Kieran M. O'Connor,et al.  Application of an empirical infiltration equation in the SMAR conceptual model , 1996 .

[25]  Xiaomeng Song,et al.  Impacts of climate change on water resources in the Yellow River basin and identification of global adaptation strategies , 2015, Mitigation and Adaptation Strategies for Global Change.

[26]  Xiaolin Yan,et al.  Attribution for decreasing streamflow of the Haihe River basin, northern China: Climate variability or human activities? , 2012 .

[27]  Fei Wang,et al.  Changes in streamflow and sediment discharge and the response to human activities in the middle reaches of the Yellow River , 2010 .

[28]  C. Woodcock,et al.  Improvement and expansion of the Fmask algorithm: cloud, cloud shadow, and snow detection for Landsats 4–7, 8, and Sentinel 2 images , 2015 .

[29]  Soroosh Sorooshian,et al.  Developing reservoir monthly inflow forecasts using artificial intelligence and climate phenomenon information , 2017 .

[30]  H. Hong,et al.  Attribution of the changes in annual streamflow in the Yangtze River Basin over the past 146 years , 2014, Theoretical and Applied Climatology.

[31]  Warren B. Cohen,et al.  Trajectory-based change detection for automated characterization of forest disturbance dynamics , 2007 .

[32]  Space–time changes in hydrological processes in response to human activities and climatic change in the south China , 2012, Stochastic Environmental Research and Risk Assessment.

[33]  Wei-Yin Loh,et al.  Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..

[34]  C. Woodcock,et al.  Continuous change detection and classification of land cover using all available Landsat data , 2014 .

[35]  Walter Boughton,et al.  Estimating runoff in ungauged catchments from rainfall, PET and the AWBM model , 2007, Environ. Model. Softw..

[36]  Heng Zhang,et al.  Dynamic runoff simulation in a changing environment: A data stream approach , 2019, Environ. Model. Softw..

[37]  Yongqiang Zhang,et al.  Predicting Surface Runoff from Catchment to Large Region , 2015 .

[38]  Junming Shao,et al.  Assessing climate impact on forest cover in areas undergoing substantial land cover change using Landsat imagery. , 2019, The Science of the total environment.

[39]  Young-Oh Kim,et al.  Comparison of hydrological models for the assessment of water resources in a data-scarce region, the Upper Blue Nile River Basin , 2017 .

[40]  João Gama,et al.  Learning with Drift Detection , 2004, SBIA.

[41]  Halil Ibrahim Erdal,et al.  Advancing monthly streamflow prediction accuracy of CART models using ensemble learning paradigms , 2013 .

[42]  S. Goward,et al.  An automated approach for reconstructing recent forest disturbance history using dense Landsat time series stacks , 2010 .

[43]  Christopher E. Holden,et al.  Including land cover change in analysis of greenness trends using all available Landsat 5, 7, and 8 images: A case study from Guangzhou, China (2000–2014) , 2016 .

[44]  Qiang Huang,et al.  Impact of climate change and human activities on runoff in the Weihe River Basin, China , 2015 .

[45]  Pan Liu,et al.  Separating the impacts of climate change and human activities on runoff using the Budyko-type equations with time-varying parameters , 2015 .

[46]  Ozgur Kisi,et al.  Applications of hybrid wavelet–Artificial Intelligence models in hydrology: A review , 2014 .

[47]  Upmanu Lall,et al.  A Tree-Ring-Based Reconstruction of Delaware River Basin Streamflow Using Hierarchical Bayesian Regression , 2013 .

[48]  Juan Frausto-Solís,et al.  Short-term streamflow forecasting: ARIMA vs neural networks , 2008 .