Reliable prediction of groundwater depth fluctuations has been an important component in sustainable water resources management. In this study, a data-driven prediction model combining discrete wavelet transform (DWT) preprocess and support vector machine (SVM) was proposed for groundwater depth forecasting. Regular artificial neural networks (ANN), regular SVM, and wavelet preprocessed artificial neural networks (WANN) models were also developed for comparison. These methods were applied to the monthly groundwater depth records over a period of 37 years from ten wells in the Mengcheng County, China. Relative absolute error (RAE), Pearson correlation coefficient (r), root mean square error (RMSE), and Nash-Sutcliffe efficiency (NSE) were adopted for model evaluation. The results indicate that wavelet preprocess extremely improved the training and test performance of ANN and SVM models. The WSVM model provided the most precise and reliable groundwater depth prediction compared with ANN, SVM, and WSVM models. The criterion of RAE, r, RMSE, and NSE values for proposed WSVM model are 0.20, 0.97, 0.18 and 0.94, respectively. Comprehensive comparisons and discussion revealed that wavelet preprocess extremely improves the prediction precision and reliability for both SVM and ANN models. The prediction result of SVM model is superior to ANN model in generalization ability and precision. Nevertheless, the performance of WANN is superior to SVM model, which further validates the power of data preprocess in data-driven prediction models. Finally, the optimal model, WSVM, is discussed by comparing its subseries performances as well as model performance stability, revealing the efficiency and universality of WSVM model in data driven prediction field.

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The synthesis of metal halide perovskite/perovskitoid (MHP) photocatalysts with well-defined morphologies and facet-specific redox activity is technically challenging. Herein, using surfactants to control the arrangement of 0D facet-shared [Bi2I9]3– dioctahedra building blocks, we successfully fabricated ordered perovskite Cs3Bi2I9 hexagonal prisms (CBI-HPs). Using Co2+ oxidation and Pt4+ reduction as redox probes, photoexcited holes were shown to spatially migrate to the edge (100) facets while photoexcited electrons migrated to the (006) basal facets, respectively. Density functional theory revealed that the built-in potential of the facet junction between (100) and (006) facets was ∼130 meV. Because of the well-separated redox facets, the photocatalytic hydrogen evolution rate of ordered CBI-HPs via hydroiodic acid splitting reached 1504.5 μmol/h/g, which is 22.1 times that of a disordered CBI photocatalyst. This work guides the rational design of high-performance MHP photocatalysts for solar energy conversion and other applications.

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