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大尺度跨境流域径流预测的迁移学习框架--敏感性分析及在数据稀缺流域的适用性

Journal of Geographical Sciences 2024年第5期34卷 963-984页

作者：马凯申朝鹏许紫月何大明 Institute of International Rivers and Eco-security Yunnan UniversityKunming 650091China Yunnan Key Laboratory of International Rivers and Transboundary Eco-security Yunnan UniversityKunming 650091China Civil and Environmental Engineering Pennsylvania State UniversityUniversity ParkPAUnited States

The imbalance in global streamflow gauge distribution and regional data scarcity,especially in large transboundary basins,challenge regional water resource management.Effectively utilizing these limited data to construct reliable models is of crucial practical im-portance.This study employs a transfer learning(TL)framework to simulate daily streamflow in the Dulong-lrrawaddy River Basin(DIRB),a less-studied transboundary basin shared by Myanmar,China,and India.Our results show that TL significantly improves streamflow pre-dictions:the optimal TL model achieves an average Nash-Sutcliffe efficiency of 0.872,showing a marked improvement in the Hkamti sub-basin.Despite data scarcity,TL achieves a mean NSE of 0.817,surpassing the 0.655 of the process-based model MIKE SHE.Addition-ally,our study reveals the importance of source model selection in TL,as different parts of the flow are affected by the diversity and similarity of data in the source model.Deep learning models,particularly TL,exhibit complex sensitivities to meteorological inputs,more accu-rately capturing non-linear relationships among multiple variables than the process-based model.Integrated gradients(IG)analysis furtherillustrates TL's ability to capture spatial het-erogeneity in upstream and downstream sub-basins and its adeptness in characterizing dif-ferent flow regimes.This study underscores the potential of TL in enhancing the under-standing of hydrological processes in large-scale catchments and highlights its value for wa-ter resource management in transboundary basins under data scarcity.

关键词： transfer learning streamflow prediction deep learning model sensitivity data scarcity international river

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Recharge and vulnerability assessment of groundwater resources in North west India:Insights from isotope-geospatial modelling approach

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Geoscience Frontiers 2024年第1期15卷 247-262页

作者： Annadasankar Roy Sitangshu Chatterjee Uday Kumar Sinha Anil Kumar Jain Hemant Mohokar Ajay Jaryal Tirumalesh Keesari Harish Jagat Pant Isotope and Radiation Application Division Bhabha Atomic Research CentreTrombayMumbai–400085India Homi Bhabha National Institute Mumbai 400094India Central Ground Water Board AhmedabadGujaratIndia

Recent studies indicate dwindling groundwater quantity and quality of the largest regional aquifer system in North West India,raising concern over freshwater availability to about 182 million population residing in this region.Widespread agricultural activities have resulted severe groundwater pollution in this area,demanding a systematic vulnerability assessment for proactive measures.Conventional vulnerability assessment models encounter drawbacks due to subjectivity,complexity,data-prerequisites,and spatial-temporal constraints.This study incorporates isotopic information into a weighted-overlay framework to overcome the above-mentioned limitations and proposes a novel vulnerability assessment model.The isotope methodology provides crucial insights on groundwater recharge mechanisms(18O and 2H)and dynamics(3H)-often ignored in vulnerability assessment.Isotopic characterisation of precipitation helped in establishing Local Meteoric Water Line(LMWL)as well as inferring contrasting recharge mechanisms operating in different aquifers.Shallow aquifer(depthdata.The central and northern parts of study area fall under least(0.29%)and extremely(1.79%)vulnerable zones respectively,while majority of the study area fall under moderate(42.71%)and highly vulnerable zones(55.20%).Model validation was performed using groundwater NO3-concentration,which showed an overall accuracy up to 82%.Monte Carlo Simulation(MCS)was performed for sensitivity analysis and permeability was found to be the most sensitive input parameter,followed by 3H,18O,and water level.Comparing the vulnerability map with Land Use Land Cover(LULC)and population density maps helped in precisely identifying the high-risk s

关键词： Agricultural pollution data scarcity Recharge mechanism Rayleigh distillation Isotope-geospatial model Vulnerability assessment

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A Conditional Generative adversarial Network for energy use in multiple buildings using scarce data

能源与人工智能(英文)

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能源与人工智能(英文) 2021年第3期5卷 119-132页

作者： Gaby Baasch Guillaume Rousseau Ralph Evins Energy and Cities Group Department of Civil EngineeringUniversity of VictoriaCanada

Building consumption data is integral to numerous applications including retrofit analysis,Smart Grid integration and optimization,and load forecasting.Still,due to technical limitations,privacy concerns and the proprietary nature of the industry,usable data is often unavailable for research and development.Generative adversarial networks(GANs)-which generate synthetic instances that resemble those from an original training dataset-have been proposed to help address this issue.Previous studies use GANs to generate building sequence data,but the models are not typically designed for time series problems,they often require relatively large amounts of input data(at least 20,000 sequences)and it is unclear whether they correctly capture the temporal behaviour of the buildings.In this work we implement a conditional temporal GAN that addresses these issues,and we show that it exhibits state-of-the-art performance on small datasets.22 different experiments that vary according to their data inputs are benchmarked using Jensen-Shannon divergence(JSD)and predictive forecasting validation error.Of these,the best performing is also evaluated using a curated set of metrics that extends those of previous work to include PCA,deep-learning based forecasting and measurements of trend and seasonality.Two case studies are included:one for residential and one for commercial buildings.The model achieves a JSD of 0.012 on the former data and 0.037 on the latter,using only 396 and 156 original load sequences,respectively.

关键词： Generative adversarial network Building load profile Machine learning data scarcity

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