Application of Entropy Theory to Multivariate Hydrologic Analysis. (Volumes I and II).

This dissertation discusses the multivariate hydrologic analysis by the entropy theory. It is divided into two major parts. The first part (Volume I) examines hydrologic frequency analysis, specifically rainfall-runoff modeling and the design of rainfall networks. The second part (Volume II) develops two flood forecasting models: the univariate streamflow model, and the bivariate rainfall-runoff model. The hydrologic frequency analysis focuses on three topics: multivariate normal distribution, multivariate exponential distribution and multivariate exponential distribution and multivariate mixed distributions. Many forms of univariate, bivariate and multivariate normal distributions are derived by using the principle of maximum entropy (POME), emphasizing the serial dependency of rainfall and runoff process, and the variable dependency among rainfall and runoff processes. The importance of the variables in the partial duration series model dependent on the cutoff level is examined by entropy and transinformation. Several entropy criteria exist in spacetime design of rainfall networks. Multivariate forms of the Marshall-Olkin exponential distributions are also derived using POME. The bivariate exponential distribution is compared with the bivariate normal distribution in the space design of rainfall networks. By combining exponential and discrete distributions, the multivariate mixed distributions are constructed. These distributions are tested on partial and annual duration series models. The flood forecasting models are developed by adjustment of equations from the maximum entropy spectral analysis. The univariate streamflow model for a long-term (monthly and seasonal) flood forecasting is developed and tested on five climatologically different watersheds, and then compared with the established time series models (ARIMA and state-space). The bivariate rainfall-runoff model, theoretically extending the univariate case, is developed for real-time forecasting, tested on five different climatological areas, and compared with the state-space model. Extensive parameter analyses for both models are given and some intriguing conceptual connections between developed models and time series models are established. Finally, comprehensive guidelines and recommendations for the future work are given

A stochastic model for sediment yield using the Principle of Maximum Entropy

An edited version of this paper was published by AGU. Copyright 1987 American Geophysical Union.The principle of maximum entropy was applied to derive a stochastic model for sediment yield from upland watersheds. By maximizing the conditional entropy subject to certain constraints, a probability distribution of sediment yield conditioned on the probability distribution of direct runoff volume was obtained. This distribution resulted in minimally prejudiced assignment of probabilities on the basis of given information. The parameters of this distribution were determined from such prior information about the direct runoff volume and sediment yield as their means and covariance. The stochastic model was verified by using three sets of field data and was compared with a bivariate normal distribution. The model yielded sediment yield reasonably accurately.This study was supported in part by funds provided by the Geological Survey, U.S. Department of Agriculture, through the Louisiana Water Resources Research Institute, under the project, A Multivariate Stochastic Analysis of Flood Magnitude, Duration and Volume

Fourier-based parametrization of convolutional neural networks for robust time series forecasting

Classical statistical models for time series forecasting most often make a number of assumptions about the data at hand, there-with, requiring intensive manual preprocessing steps prior to modeling. As a consequence, it is very challenging to come up with a more generic forecasting framework. Extensive hyperparameter optimization and ensemble architectures are common strategies to tackle this problem, however, this comes at the cost of high computational complexity. Instead of optimizing hyperparameters by training multiple models, we propose a method to estimate optimal hyperparameters directly from the characteristics of the time series at hand. To that end, we use Convolutional Neural Networks (CNNs) for time series forecasting and determine a part of the network layout based on the time series’ Fourier coefficients. Our approach significantly reduces the amount of required model configuration time and shows competitive performance on time series data across various domains. A comparison to popular, state of the art forecasting algorithms reveals further improvements in runtime and practicability

Ensembles of recurrent neural networks for robust time series forecasting

Time series forecasting is a problem that is strongly dependent on the underlying process which generates the data sequence. Hence,finding good model fits often involves complex and time consuming tasks such as extensive data preprocessing, designing hybrid models, or heavy parameter optimization. Long Short-Term Memory (LSTM), a variant of recurrent neural networks (RNNs), provide state of the art forecasting performance without prior assumptions about the data distribution. LSTMs are, however, highly sensitive to the chosen network architecture and parameter selection, which makes it difficult to come up with a one-size-fits-all solution without sophisticated optimization and parameter tuning. To overcome these limitations, we propose an ensemble architecture that combines forecasts of a number of differently parameterized LSTMs to a robust final estimate which, on average, performs better than the majority of the individual LSTM base learners, and provides stable results across different datasets. The approach is easily parallelizable and we demonstrate its effectiveness on several real-world data sets

Uncovering English-medium instruction: glocal issues in higher education

This book draws on a range of theoretical and empirical insights to explore the implications of English-medium instruction in higher education and how to capitalize on its strengths and respond to its challenges. It opens up new avenues for research relevant to all educational institutions undergoing change in this field