Identifying the Sensitivity of Ensemble Streamflow Prediction by Artificial Intelligence

Sustainable water resources management is facing a rigorous challenge due to global climate change. Nowadays, improving streamflow predictions based on uneven precipitation is an important task. The main purpose of this study is to integrate the ensemble technique concept into artificial neural networks for reducing model uncertainty in hourly streamflow predictions. The ensemble streamflow predictions are built following two steps: (1) Generating the ensemble members through disturbance of initial weights, data resampling, and alteration of model structure; (2) consolidating the model outputs through the arithmetic average, stacking, and Bayesian model average. This study investigates various ensemble strategies on two study sites, where the watershed size and hydrological conditions are different. The results help to realize whether the ensemble methods are sensitive to hydrological or physiographical conditions. Additionally, the applicability and availability of the ensemble strategies can be easily evaluated in this study. Among various ensemble strategies, the best ESP is produced by the combination of boosting (data resampling) and Bayesian model average. The results demonstrate that the ensemble neural networks greatly improved the accuracy of streamflow predictions as compared to a single neural network, and the improvement made by the ensemble neural network is about 19–37% and 20–30% in Longquan Creek and Jinhua River watersheds, respectively, for 1–3 h ahead streamflow prediction. Moreover, the results obtained from different ensemble strategies are quite consistent in both watersheds, indicating that the ensemble strategies are insensitive to hydrological and physiographical factors. Finally, the output intervals of ensemble streamflow prediction may also reflect the possible peak flow, which is valuable information for flood prevention.

Average Method of Mixed Sampling Distribution Bayesian Model

Based on the study of Bayesian model average (BMA), this paper proposes to mix the prior distribution and sampling distribution to obtain the average method of the mixed sampling distribution Bayesian model overcoming the problem that traditional econometric modeling method does not explicitly consider the uncertainty of the model. If all the alternative models have the same parametric form, then the new Bayesian estimation will degenerate into the BMA estimator. The empirical results show that the method is better than Bayesian model average.

CÁC YẾU TỐ KINH TẾ VĨ MÔ TÁC ĐỘNG ĐẾN GIẢM NGHÈO Ở LÂM ĐỒNG

Nghiên cứu này xem xét các yếu tố kinh tế vĩ mô tác động đến giảm nghèo của tỉnh Lâm Đồng, được xác định trên cơ sở xây dựng mô hình hồi qui bội tối ưu bằng phương pháp BMA (Bayesian Model Average) dựa vào kết quả các chỉ số về thu nhập, thất nghiệp (việc làm), lạm phát và chất lượng nguồn nhân lực tại Lâm Đồng. Kết quả nghiên cứu đã tìm thấy 2 yếu tố quan trọng, có ý nghĩa thống kê và giá trị thực tiễn tác động đến giảm nghèo của tỉnh Lâm Đồng theo mức độ tầm quan trọng của từng trọng số, đó là: Thu nhập bình quân và Chất lượng nguồn nhân lực. Cuối cùng tác giả trình bày hàm ý và khuyến nghị một số giải pháp từ kết quả nghiên cứu.

ENSURF: multi-model sea level forecast – implementation and validation results for the IBIROOS and Western Mediterranean regions

ENSURF (Ensemble SURge Forecast) is a multi-model application for sea level forecast that makes use of several storm surge or circulation models and near-real time tide gauge data in the region, with the following main goals: 1. providing easy access to existing forecasts, as well as to its performance and model validation, by means of an adequate visualization tool; 2. generation of better forecasts of sea level, including confidence intervals, by means of the Bayesian Model Average technique (BMA). The Bayesian Model Average technique generates an overall forecast probability density function (PDF) by making a weighted average of the individual forecasts PDF's; the weights represent the Bayesian likelihood that a model will give the correct forecast and are continuously updated based on the performance of the models during a recent training period. This implies the technique needs the availability of sea level data from tide gauges in near-real time. The system was implemented for the European Atlantic facade (IBIROOS region) and Western Mediterranean coast based on the MATROOS visualization tool developed by Deltares. Results of validation of the different models and BMA implementation for the main harbours are presented for these regions where this kind of activity is performed for the first time. The system is currently operational at Puertos del Estado and has proved to be useful in the detection of calibration problems in some of the circulation models, in the identification of the systematic differences between baroclinic and barotropic models for sea level forecasts and to demonstrate the feasibility of providing an overall probabilistic forecast, based on the BMA method.