A computer vision-based approach to fusing spatiotemporal data for hydrological modeling

Jiang, Shijie1,2; Zheng, Yi1,3; Babovic, Vladan2; Tian, Yong1,3; Han, Feng1

2018-12

10.1016/j.jhydrol.2018.09.064

JOURNAL OF HYDROLOGY   影响因子和分区

0022-1694

1879-2707

This study develops a novel approach to data-driven hydrological modeling. The approach adopts the feature representation technique in computer vision to effectively exploit spatial information contained in time-variant input data fields and seamlessly fuse multisource information via machine learning. The new approach overcomes a major limitation of existing approaches in which the spatial heterogeneity of input variables cannot be sufficiently accounted for. The approach is applied to predict the streamflow in a watershed on the northern margin of the Qinghai-Tibetan Plateau, and its performance is compared with various data-driven and process-based models. The major findings are as follows. First, the new approach represents a general framework for the fusion of multisource spatiotemporal data for hydrological modeling and demonstrates great potential to incorporate fast-growing environmental big data. Second, the new approach demonstrates satisfactory short-term forecasting, long-term simulation, and transfer learning performances and is promising for addressing predictions in ungauged basins. Third, the predictors, including precipitation, temperature, leaf area index, and historical streamflow, play markedly distinct roles in modeling streamflow with the novel approach. Finally, topographic information is not a necessary model input in the proposed approach because spatial patterns can be well embodied by other inputs (e.g., temperature) that have high similarities with topography. This study represents the first attempt to bring computer vision into data-driven hydrological modeling and may inspire future studies in this promising direction.

Computer vision Data fusion Hydrological modeling Machine learning Heihe River Basin Environmental big data

SCI ; EI

英语

第一 ; 通讯

Shenzhen Municipal Science and Technology Innovation Committee[JCYJ20160530190411804]

Engineering ; Geology ; Water Resources

Engineering, Civil ; Geosciences, Multidisciplinary ; Water Resources

WOS:000454753900003

ELSEVIER SCIENCE BV

20184105932181

Artificial intelligence ; Computer vision ; Data fusion ; Forecasting ; Hydrology ; Learning systems ; Plants (botany) ; Rain ; Stream flow

Waterways:407.2 ; Precipitation:443.3 ; Computer Software, Data Handling and Applications:723

ENGINEERING

Web of Science

1.Southern Univ Sci & Technol, State Environm Protect Key Lab Integrated Surface, Sch Environm Sci & Engn, Shenzhen 518055, Peoples R China
2.Natl Univ Singapore, Dept Civil & Environm Engn, Singapore 117576, Singapore
3.Southern Univ Sci & Technol, Shenzhen Municipal Engn Lab Environm IoT Technol, Shenzhen 518055, Peoples R China

Jiang, Shijie,Zheng, Yi,Babovic, Vladan,et al. A computer vision-based approach to fusing spatiotemporal data for hydrological modeling[J]. JOURNAL OF HYDROLOGY,2018,567:25-40.

Jiang, Shijie,Zheng, Yi,Babovic, Vladan,Tian, Yong,&Han, Feng.(2018).A computer vision-based approach to fusing spatiotemporal data for hydrological modeling.JOURNAL OF HYDROLOGY,567,25-40.

Jiang, Shijie,et al."A computer vision-based approach to fusing spatiotemporal data for hydrological modeling".JOURNAL OF HYDROLOGY 567(2018):25-40.