English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Although Gravity Recovery and Climate Experiment (GRACE) can provide accurate estimates in water storage, there are about 11 months gaps between GRACE and its successor GRACE-Follow On (GRACE-FO). To improve the accuracy of bridging the gaps, this study combines the partial least squares regression (PLSR) model with the learning-based models for the first time, and construct an artificial neural network selective joint prediction model. To this end, the performance of combination of PLSR and nonlinear autoregressive with exogenous input (NARX) models, the back propagation (BP) models, and multiply linear regression (MLR) models are compared on both grid and region scales. Results indicate the proportion of &#x201C;qualified&#x201D; and above predicted by PLSR combined with NARX on grid scale increased by 32.71% and 24.65% compared to the other two models. Finally, PLSR joint NARX model performs the best against GRACE observations, then employed to predict and bridge the TWSA gaps from July 2017 to December 2019 in northwest, southwest and central of Yunnan. The NSE values between predicted TWSA (terrestrial water storage anomalies) and GRACE-FO are increased by 55.10%, 93.10% and 133.33% compared to modeled TWSA and GRACE. This study provides a new perspective for filling the GRACE gap, and for selecting optimal variables in other relative hydrologic studies.

ieee access

Improving the Reliability of the Prediction of Terrestrial Water Storage in Yunnan Using the Artificial Neural Network Selective Joint Prediction Model