Multivariate overall and dependence trend tests, applied to hydrology.

Given climate change, trend detection is gaining increasing attention in the context of multivariate frequency analysis. In this paper, we propose new statistical tests for multivariate trend detection. The first one, a multivariate overall trend (MOT) test, is designed to detect trend in all components of the multivariate distribution (margins and dependence structure) whereas the second test is a multivariate dependence trend (MDT) test focusing on detecting trend in the dependence structure. A simulation study is used to evaluate the performance of the proposed tests. Results show that the proposed MOT test performs well when trend is present in margins, in the dependence structure and/or in both. Likewise, results of the proposed MDT test indicate a higher power when the trend is in the dependence structure. Moreover, an application to a real-world dataset is provided. Performing the proposed tests with the univariate tests provides a complete overview of trend detection

Machine and deep learning for modelling heat-health relationships.

Extreme heat events pose a significant threat to population health that is amplified by climate change. Traditionally, statistical models have been used to model heat-health relationships, but they do not consider potential interactions between temperature-related and air pollution predictors. Artificial intelligence (AI) methods, which have gained popularity for health applications in recent years, can account for these complex and non-linear interactions, but have been underutilized in modelling heat-related health impacts. In this paper, six machine and deep learning models were considered to model the heat-mortality relationship in Montreal (Canada) and compared to three statistical models commonly used in the field. Decision Tree (DT), Random Forest (RF), Gradient Boosting Machine (GBM), Single- and Multi-Layer Perceptrons (SLP and MLP), Long Short-Term Memory (LSTM), Generalized Linear and Additive Models (GLM and GAM), and Distributed Lag Non-Linear Model (DLNM) were employed. Heat exposure was characterized by air temperature, relative humidity and wind speed, while air pollution was also included in the models using five pollutants. The results confirmed that air temperature at lags of up to 3 days was the most important variable for the heat-mortality relationship in all models. NO₂ concentration and relative humidity (at lags 1 to 3 days) were also particularly important. Ensemble tree-based methods (GBM and RF) outperformed other approaches to model daily mortality during summer months based on three performance criteria. However, a partial validation during two recent major heatwaves highlighted that non-linear statistical models (GAM and DLNM) and simpler decision tree may more closely reproduce the spike of mortality observed during such events. Hence, both machine learning and statistical models are relevant for modelling heat-health relationships depending on the end user goal. Such extensive comparative analysis should be extended to other health outcomes and regions

Streamflow forecasting using functional regression

Streamflow, as a natural phenomenon, is continuous in time and so are the meteorological variables which influence its variability. In practice, it can be of interest to forecast the whole flow curve instead of points (daily or hourly). To this end, this paper introduces the functional linear models and adapts it to hydrological forecasting. More precisely, functional linear models are regression models based on curves instead of single values. They allow to consider the whole process instead of a limited number of time points or features. We apply these models to analyse the flow volume and the whole streamflow curve during a given period by using precipitations curves. The functional model is shown to lead to encouraging results. The potential of functional linear models to detect special features that would have been hard to see otherwise is pointed out. The functional model is also compared to the artificial neural network approach and the advantages and disadvantages of both models are discussed. Finally, future research directions involving the functional model in hydrology are presented

Evaluation of a Depth-Based Multivariate k

A nonparametric simulation model (k-nearest neighbor resampling, KNNR) for water quality analysis involving geographic information is suggested to overcome the drawbacks of parametric models. Geographic information is, however, not appropriately handled in the KNNR nonparametric model. In the current study, we introduce a novel statistical notion, called a “depth function,” in the classical KNNR model to appropriately manipulate geographic information in simulating stormwater quality. An application is presented for a case study of the total suspended solids throughout the entire United States. The stormwater total suspended solids concentration data indicated that the proposed model significantly improves the simulation performance compared with the existing KNNR model