Quantifying uncertainty in multivariate quantile estimation of hydrometeorological extremes via copula: A comparison between bootstrapping and Markov chain Monte Carlo

Extreme rainfall event forecasting is important as these events are responsible for causing floods, landslides, and other hazards with substantial socio-economic consequences. The intricate nature of rainfall occurrences makes it more difficult to forecast accurately, especially when it comes to extreme rainfall. This study investigates the application of the Quantile Perturbation Method (QPM) along with the Long Short-Term Memory (LSTM) networks to forecast extreme rainfall anomalies. This methodology utilizes the strength of QPM to decipher oscillations in time series of extreme rainfall to identify anomalies, which are then forecasted using LSTM. The model was developed for the Ajay River basin as a case study based on historical rainfall data from 1901–2022. To determine the best model, several experiments with various configurations were conducted. Performance metrics such as Nash-Sutcliffe efficiency (NSE), correlation coefficient (R), and root mean square error (RMSE) were utilized for model evaluations. The QPM-LSTM model was compared against other combined machine learning models, including Artificial Neural Networks (ANN) and Support Vector Regression (SVR). The investigation demonstrated satisfactory predictive performance by the QPM-LSTM model, achieving NSE, R, and RMSE values of 0.87, 0.93, and 7.26, respectively. Compared to the other evaluated models, these results highlighted the potential of the QPM-LSTM model as a valuable tool for forecasting extreme rainfall anomalies, offering significant benefits for water resource management and other sectors vulnerable to extreme rainfall events.

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Enhancing Remote Sensing Water Quality Inversion through Integration of Multisource Spatial Covariates: A Case Study of Hong Kong’s Coastal Nutrient Concentrations

Zhang,

Li,

Yang

et al. 2024

Remote Sensing

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The application of remote sensing technology for water quality monitoring has attracted much attention recently. Remote sensing inversion in coastal waters with complex hydrodynamics for non-optically active parameters such as total nitrogen (TN) and total phosphorus (TP) remains a challenge. Existing studies build the relationships between remote sensing spectral data and TN/TP directly or indirectly via the mediation of optically active parameters (e.g., total suspended solids). Such models are often prone to overfitting, performing well with the training set but underperforming with the testing set, even though both datasets are from the same region. Using the Hong Kong coastal region as a case study, we address this issue by incorporating spatial covariates such as hydrometeorological and locational variables as additional input features for machine learning-based inversion models. The proposed model effectively alleviates overfitting while maintaining a decent level of accuracy (R2 exceeding 0.7) during the training, validation and testing steps. The gap between model R2 values in training and testing sets is controlled within 7%. A bootstrap uncertainty analysis shows significantly improved model performance as compared to the model with only remote sensing inputs. We further employ the Shapely Additive Explanations (SHAP) analysis to explore each input’s contribution to the model prediction, verifying the important role of hydrometeorological and locational variables. Our results provide a new perspective for the development of remote sensing inversion models for TN and TP in similar coastal waters.

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Quantifying uncertainty in multivariate quantile estimation of hydrometeorological extremes via copula: A comparison between bootstrapping and Markov chain Monte Carlo

Cited by 3 publications

References 63 publications

Spatially distributed bivariate meteorological drought analysis using copula technique in a semi-arid river basin of West Bengal, India

Spatially distributed bivariate meteorological drought analysis using copula technique in a semi-arid river basin of West Bengal, India

Extreme rainfall forecasting using a hybrid model approach - A case study of the Ajay River basin

Enhancing Remote Sensing Water Quality Inversion through Integration of Multisource Spatial Covariates: A Case Study of Hong Kong’s Coastal Nutrient Concentrations

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