ﻻ يوجد ملخص باللغة العربية
Longitudinal Dispersion(LD) is the dominant process of scalar transport in natural streams. An accurate prediction on LD coefficient(Dl) can produce a performance leap in related simulation. The emerging machine learning(ML) techniques provide a self-adaptive tool for this problem. However, most of the existing studies utilize an unproved quaternion feature set, obtained through simple theoretical deduction. Few studies have put attention on its reliability and rationality. Besides, due to the lack of comparative comparison, the proper choice of ML models in different scenarios still remains unknown. In this study, the Feature Gradient selector was first adopted to distill the local optimal feature sets directly from multivariable data. Then, a global optimal feature set (the channel width, the flow velocity, the channel slope and the cross sectional area) was proposed through numerical comparison of the distilled local optimums in performance with representative ML models. The channel slope is identified to be the key parameter for the prediction of LDC. Further, we designed a weighted evaluation metric which enables comprehensive model comparison. With the simple linear model as the baseline, a benchmark of single and ensemble learning models was provided. Advantages and disadvantages of the methods involved were also discussed. Results show that the support vector machine has significantly better performance than other models. Decision tree is not suitable for this problem due to poor generalization ability. Notably, simple models show superiority over complicated model on this low-dimensional problem, for their better balance between regression and generalization.
A better understanding of dispersion in natural streams requires knowledge of longitudinal dispersion coefficient(LDC). Various methods have been proposed for predictions of LDC. Those studies can be grouped into three types: analytical, statistical
We present paired learning and inference algorithms for significantly reducing computation and increasing speed of the vector dot products in the classifiers that are at the heart of many NLP components. This is accomplished by partitioning the featu
In this work we demonstrate the efficacy of neural networks in the characterization of dispersive media. We also develop a neural network to make predictions for input probe pulses which propagate through a nonlinear dispersive medium, which may be a
Earthquakes can be detected by matching spatial patterns or phase properties from 1-D seismic waves. Current earthquake detection methods, such as waveform correlation and template matching, have difficulty detecting anomalous earthquakes that are no
In machine learning applications for online product offerings and marketing strategies, there are often hundreds or thousands of features available to build such models. Feature selection is one essential method in such applications for multiple obje