Application of minimum relative entropy theory for streamflow forecasting

This paper develops and applies the minimum relative entropy (MRE) theory with spectral power as a random variable for streamflow forecasting. The MRE theory consists of (1) hypothesizing a prior probability distribution for the random variable, (2) determining the spectral power distribution, (3) extending the autocorrelation function, and (4) doing forecasting. The MRE theory was verified using streamflow data from the Mississippi River watershed. The exponential distribution was chosen as a prior probability in applying the MRE theory by evaluating the historical data of the Mississippi River. If no prior information is given, the MRE theory is equivalent to the Burg entropy (BE) theory. The spectral density obtained by the MRE theory led to higher resolution than did the BE theory. The MRE theory did not miss the largest peak at 1/12th frequency, which is the main periodicity of streamflow of the Mississippi River, but the BE theory sometimes did. The MRE theory was found to be capable of forecasting monthly streamflow with a lead time from 12 to 48 months. The coefficient of determination (r ²) between observed and forecasted stream flows was 0.912 for Upper Mississippi River and was 0.855 for Lower Mississippi River. Both MRE and BE theories were generally more reliable and had longer forecasting lead times than the autoregressive (AR) method. The forecasting lead time for MRE and BE could be as long as 48–60 months, while it was less than 48 months for the AR method. However, BE was comparable to MRE only when observations fitted the AR process well. The MRE theory provided more reliable forecasts than did the BE theory, and the advantage of using MRE is more significant for downstream flows with irregular flow patterns or where the periodicity information is limited. The reliability of monthly streamflow forecasting was the highest for MRE, followed by BE followed by AR.     

Minimum relative entropy and probabilistic inversion in groundwater hydrology

The similarity between maximum entropy (MaxEnt) and minimum relative entropy (MRE) allows recent advances in probabilistic inversion to obviate some of the shortcomings in the former method. The purpose of this paper is to review and extend the theory and practice of minimum relative entropy. In this regard, we illustrate important philosophies on inversion and the similarly and differences between maximum entropy, minimum relative entropy, classical smallest model (SVD) and Bayesian solutions for inverse problems. MaxEnt is applicable when we are determining a function that can be regarded as a probability distribution. The approach can be extended to the case of the general linear problem and is interpreted as the model which fits all the constraints and is the one model which has the greatest multiplicity or “spreadout” that can be realized in the greatest number of ways. The MRE solution to the inverse problem differs from the maximum entropy viewpoint as noted above. The relative entropy formulation provides the advantage of allowing for non-positive models, a prior bias in the estimated pdf and `hard' bounds if desired. We outline how MRE can be used as a measure of resolution in linear inversion and show that MRE provides us with a method to explore the limits of model space. The Bayesian methodology readily lends itself to the problem of updating prior probabilities based on uncertain field measurements, and whose truth follows from the theorems of total and compound probabilities. In the Bayesian approach information is complete and Bayes' theorem gives a unique posterior pdf. In comparing the results of the classical, MaxEnt, MRE and Bayesian approaches we notice that the approaches produce different results. In␣comparing MaxEnt with MRE for Jayne's die problem we see excellent comparisons between the results. We compare MaxEnt, smallest model and MRE approaches for the density distribution of an equivalent spherically-symmetric earth and for the contaminant plume-source problem. Theoretical comparisons between MRE and Bayesian solutions for the case of the linear model and Gaussian priors may show different results. The Bayesian expected-value solution approaches that of MRE and that of the smallest model as the prior distribution becomes uniform, but the Bayesian maximum aposteriori (MAP) solution may not exist for an underdetermined case with a uniform prior.     

Minimum relative entropy and probabilistic inversion in groundwater hydrology

The similarity between maximum entropy (MaxEnt) and minimum relative entropy (MRE) allows recent advances in probabilistic inversion to obviate some of the shortcomings in the former method. The purpose of this paper is to review and extend the theory and practice of minimum relative entropy. In this regard, we illustrate important philosophies on inversion and the similarly and differences between maximum entropy, minimum relative entropy, classical smallest model (SVD) and Bayesian solutions for inverse problems. MaxEnt is applicable when we are determining a function that can be regarded as a probability distribution. The approach can be extended to the case of the general linear problem and is interpreted as the model which fits all the constraints and is the one model which has the greatest multiplicity or “spreadout” that can be realized in the greatest number of ways. The MRE solution to the inverse problem differs from the maximum entropy viewpoint as noted above. The relative entropy formulation provides the advantage of allowing for non-positive models, a prior bias in the estimated pdf and `hard' bounds if desired. We outline how MRE can be used as a measure of resolution in linear inversion and show that MRE provides us with a method to explore the limits of model space. The Bayesian methodology readily lends itself to the problem of updating prior probabilities based on uncertain field measurements, and whose truth follows from the theorems of total and compound probabilities. In the Bayesian approach information is complete and Bayes' theorem gives a unique posterior pdf. In comparing the results of the classical, MaxEnt, MRE and Bayesian approaches we notice that the approaches produce different results. In␣comparing MaxEnt with MRE for Jayne's die problem we see excellent comparisons between the results. We compare MaxEnt, smallest model and MRE approaches for the density distribution of an equivalent spherically-symmetric earth and for the contaminant plume-source problem. Theoretical comparisons between MRE and Bayesian solutions for the case of the linear model and Gaussian priors may show different results. The Bayesian expected-value solution approaches that of MRE and that of the smallest model as the prior distribution becomes uniform, but the Bayesian maximum aposteriori (MAP) solution may not exist for an underdetermined case with a uniform prior.     

A univariate model for long-term streamflow forecasting

This paper, the first in a series of two, employs the principle of maximum entropy (POME) via maximum entropy spectral analysis (MESA) to develop a univariate model for long-term streamflow forecasting. Three cases of streamflow forecasting are investigated: forward forecasting, backward forecasting (or reconstruction) and intermittent forecasting (or filling in missing records). Application of the model is discussed in the second paper.     

Wavelet regression model as an alternative to neural networks for monthly streamflow forecasting

The accuracy of the wavelet regression (WR) model in monthly streamflow forecasting is investigated in the study. The WR model is improved combining the two methods—the discrete wavelet transform (DWT) model and the linear regression (LR) model—for 1‐month‐ahead streamflow forecasting. In the first part of the study, the results of the WR model are compared with those of the single LR model. Monthly flow data from two stations, Gerdelli Station on Canakdere River and Isakoy Station on Goksudere River, in Eastern Black Sea region of Turkey are used in the study. The comparison results reveal that the WR model could increase the forecast accuracy of the LR model. In the second part of the study, the accuracy of the WR model is compared with those of the artificial neural networks (ANN) and auto‐regressive (AR) models. On the basis of the results, the WR is found to be better than the ANN and AR models in monthly streamflow forecasting. Copyright © 2009 John Wiley & Sons, Ltd.     

Radiance data assimilation for operational snow and streamflow forecasting

Estimation of seasonal snowpack, in mountainous regions, is crucial for accurate streamflow prediction. This paper examines the ability of data assimilation (DA) of remotely sensed microwave radiance data to improve snow water equivalent prediction, and ultimately operational streamflow forecasts. Operational streamflow forecasts in the National Weather Service River Forecast Center (NWSRFC) are produced with a coupled SNOW17 (snow model) and SACramento Soil Moisture Accounting (SAC-SMA) model. A comparison of two assimilation techniques, the ensemble Kalman filter (EnKF) and the particle filter (PF), is made using a coupled SNOW17 and the microwave emission model for layered snow pack (MEMLS) model to assimilate microwave radiance data. Microwave radiance data, in the form of brightness temperature (TB), is gathered from the advanced microwave scanning radiometer-earth observing system (AMSR-E) at the 36.5 GHz channel. SWE prediction is validated in a synthetic experiment. The distribution of snowmelt from an experiment with real data is then used to run the SAC-SMA model. Several scenarios on state or joint state-parameter updating with TB data assimilation to SNOW-17 and SAC-SMA models were analyzed, and the results show potential benefit for operational streamflow forecasting.     

Non-stationary response of structures excited by random seismic processes with time variable frequency content

Seismic random processes are characterized by high non-stationarity and, in most cases, by a marked variability of frequency content. The hypothesis modeling seismic signal as a simple product of a stationary signal and a deterministic modulation function, consequently, is hardly ever applicable. Several mathematical models aimed at expressing the recorded process by means of a system of stationary random processes and deterministic amplitude and frequency modulations are proposed. Models oriented into the frequency domain with subsequent response analysis based on integral spectral resolution and models oriented into the time domain based on the multicomponent resolution are investigated. The resolution into individual components (non-stationary signals) is carried out by three methods. The resolution into intrinsic mode functions seems to possess the best characteristics and yields results almost not differing from the results obtained by stochastic simulation. An example of the seismic response of an existing bridge obtained by two older models and three variants of multicomponent resolution is given.     

Daily streamflow forecasting using a wavelet transform and artificial neural network hybrid models

Abstract

New wavelet and artificial neural network (WA) hybrid models are proposed for daily streamflow forecasting at 1, 3, 5 and 7 days ahead, based on the low-frequency components of the original signal (approximations). The results show that the proposed hybrid models give significantly better results than the classical artificial neural network (ANN) model for all tested situations. For short-term (1-day ahead) forecasts, information on higher-frequency signal components was essential to ensure good model performance. However, for forecasting more days ahead, lower-frequency components are needed as input to the proposed hybrid models. The WA models also proved to be effective for eliminating the lags often seen in daily streamflow forecasts obtained by classical ANN models. 

Editor D. Koutsoyiannis; Associate editor L. See

Citation Santos, C.A.G. and Silva, G.B.L., 2013. Daily streamflow forecasting using a wavelet transform and artificial neural network hybrid models. Hydrological Sciences Journal, 59 (2), 312–324.     

Complexity as a streamflow metric of hydrologic alteration

We explore the potential of using a complexity measure from statistical physics as a streamflow metric of basin-scale hydrologic alteration. The complexity measure that we employ is a non-trivial function of entropy. To determine entropy, we use the so-called permutation entropy (PE) approach. The PE approach is desirable in this case since it accounts for temporal streamflow information and it only requires a weak form of stationarity to be satisfied. To compute the complexity measure and assess hydrologic alteration, we employ daily streamflow records from 22 urban basins, located in the metropolitan areas of the cities of Baltimore, Philadelphia, and Washington DC, in the United States. We use urbanization to represent hydrologic alteration since urban basins are characterized by varied and often pronounced human impacts. Based on our application of the complexity measure to urban basins, we find that complexity tends to decline with increasing hydrologic alteration while entropy rises. According to this evidence, heavily urbanized basins tend to be temporally less complex (less ordered or structured) and more random than basins with low urbanization. This complexity loss may have important implications for stream ecosystems whose ability to provide ecosystem services depend on the flow regime. We also find that the complexity measure performs better in detecting alteration to the streamflow than more conventional metrics (e.g., variance and median of streamflow). We conclude that complexity is a useful streamflow metric for assessing basin-scale hydrologic alteration.     

A cross entropy method for flood frequency analysis

The cross-entropy method with fractile constraints has been developed to estimate a random variable when the data are a set of independent observations of the variable. The method can claim several advantages over existing methods. It uses a reference distribution like the prior distribution in Bayesian analysis and likewise generates a posterior distribution.The method is of interest, in particular, because it satisfies two fundamental requirements for selfconsistency in the analysis of a probabilistic system based on data: a principle of invariance and a principle of data monotonicity.The method is applied to flood analysis. Robustness of the minimum cross-entropy method is compared with other methods: the methods of moments and the maximum likehood.     

A nonlinear time series analysis using two‐stage genetic algorithms for streamflow forecasting

Streamflow forecasting is very important for the management of water resources: high accuracy in flow prediction can lead to more effective use of water resources. Hydrological data can be classified as non‐steady and nonlinear, thus this study applied nonlinear time series models to model the changing characteristics of streamflows. Two‐stage genetic algorithms were used to construct nonlinear time series models of 10‐day streamflows of the Wu‐Shi River in Taiwan. Analysis verified that nonlinear time series are superior to traditional linear time series. It is hoped that these results will be useful for further applications. Copyright © 2008 John Wiley & Sons, Ltd.     

Adjusting wavelet‐based multiresolution analysis boundary conditions for long‐term streamflow forecasting

We propose a novel technique for improving a long‐term multi‐step‐ahead streamflow forecast. A model based on wavelet decomposition and a multivariate Bayesian machine learning approach is developed for forecasting the streamflow 3, 6, 9, and 12 months ahead simultaneously. The inputs of the model utilize only the past monthly streamflow records. They are decomposed into components formulated in terms of wavelet multiresolution analysis. It is shown that the model accuracy can be increased by using the wavelet boundary rule introduced in this study. A simulation study is performed to evaluate the effects of different wavelet boundary rules using synthetic and real streamflow data from the Yellowstone River in the Uinta Basin in Utah. The model based on the combination of wavelet and Bayesian machine learning regression techniques is compared with that of the wavelet and artificial neural networks‐based model. The robustness of the models is evaluated. Copyright © 2015 John Wiley & Sons, Ltd.     

复杂变截面塔式结构振动基频实用算法研究

在Rayleigh能量法和Southwell频率合成法的基础上,以满足边界条件的静挠度函数为位移函数,推导出了底部固定、顶端带有附加质量的变截面塔式结构自振基频解析计算公式.结构的振动基频可表示成由各变形元和惯性元组成的子系统频率的合成.如果塔身截面变化复杂,可以将塔身沿高度方向划分成数个单元,以分段求和取代积分.计算...     

The constant temperature-sum as a basis for forecasting flowering date

Summary Hertlein's thesis is discussed and criticized on points where apparently significance is attached to results which require stronger statistical proofs.     

Hydrological data assimilation with the Ensemble Square-Root-Filter: Use of streamflow observations to update model states for real-time flash flood forecasting

The objective of the study is to evaluate the potential of a data assimilation system for real-time flash flood forecasting over small watersheds by updating model states. To this end, the Ensemble Square-Root-Filter (EnSRF) based on the Ensemble Kalman Filter (EnKF) technique was coupled to a widely used conceptual rainfall-runoff model called HyMOD. Two small watersheds susceptible to flash flooding from America and China were selected in this study. The modeling and observational errors were considered in the framework of data assimilation, followed by an ensemble size sensitivity experiment. Once the appropriate model error and ensemble size was determined, a simulation study focused on the performance of a data assimilation system, based on the correlation between streamflow observation and model states, was conducted. The EnSRF method was implemented within HyMOD and results for flash flood forecasting were analyzed, where the calibrated streamflow simulation without state updating was treated as the benchmark or nature run. Results for twenty-four flash-flood events in total from the two watersheds indicated that the data assimilation approach effectively improved the predictions of peak flows and the hydrographs in general. This study demonstrated the benefit and efficiency of implementing data assimilation into a hydrological model to improve flash flood forecasting over small, instrumented basins with potential application to real-time alert systems.     

Technology for monitoring and forecasting strong earthquakes in Russia with the use of the seismic entropy method

The technology which is being developed based on the seismic entropy method for monitoring and forecasting the earthquakes in the territory of Russia is described. This technology relies on seismostatistics and makes it possible to automate the monitoring system and to efficiently tap the networks of ground-based and ground-and-satellite-based observations of operative precursors. The main seismic systems responsible for the preparation of the strong earthquakes with magnitudes М ≥ 5.5 are described. The track and energy diagrams constructed for each seismic system provide the means for monitoring the preparation and forecasting the strong earthquakes in the real-time mode. Forty-four seismic systems controlling almost all seismically hazardous regions in Russia were identified and tested in real time during the period from 2010 to 2015. The guidelines for the practical application of the results of monitoring and forecasting are developed.     

Changes in hydrology and streamflow as predicted by a modelling experiment forced with climate models

Changes in precipitation and temperature have direct effects on crop water use, water stress, crop yield, evapotranspiration, water nutrient dynamics and other indicators. This study, built on a modelling framework with the Soil and Watershed Assessment Tool (SWAT) model for the Raccoon River Watershed in central Iowa, a typical US Midwestern agricultural watershed, examines the watershed response to changes in meteorological inputs from an ensemble of ten global climate models under the A1B scenario. Changes in climate were directly applied to observations (the delta change method) assuming that the estimates of climate change are reliable even if the simulated current climate may be biased. The ensemble average for the mid‐century (1946–1965) predicted 0.7% increase in daily precipitation (monthly variation from ?11.3% to +19.5%) and 2.78 °C increase in average temperature over the entire watershed. These predictions were translated through a well‐calibrated SWAT modelling setup into 22% decrease in snowfall, 16% decrease in surface runoff, 18% decrease in baseflow, 8% increase in evapotranspiration and 17% decrease in total water yield. The spatial impact at the subwatershed level revealed a wide variation (but no defined trend) with decrease in water yield that ranged from 10% to 23%. Flow near the watershed outlet (Van Meter, Iowa) is expected to decline by 17% on an average annual basis with the highest impact occurring during summer months with a maximum 39% reduction in August. Changes in climate were found to have a clear and significant impact signal of decreasing streamflow at the watershed outlet with far‐reaching implication for drinking water supplies for the central Iowa communities. Copyright © 2013 John Wiley & Sons, Ltd.     

Impact of complexity on daily and multi-step forecasting of streamflow with chaotic,stochastic, and black-box models

Despite significant research advances achieved during the last decades, seemingly inconsistent forecasting results related to stochastic, chaotic, and black-box approaches have been reported. Herein, we attempt to address the entropy/complexity resulting from hydrological and climatological conditions. Accordingly, mutual information function, correlation dimension, averaged false nearest neighbor with E1 and E2 quantities, and complexity analysis that uses sample entropy coupled with iterative amplitude adjusted Fourier transform were employed as nonlinear deterministic identification tools. We investigated forecasting of daily streamflow for three climatologically different Swedish rivers, Helge, Ljusnan, and Kalix Rivers using self-exciting threshold autoregressive (SETAR), k-nearest neighbor (k-nn), and artificial neural networks (ANN). The results suggest that the streamflow in these rivers during the 1957–2012 period exhibited dynamics from low to high complexity. Specifically, (1) lower complexity lead to higher predictability at all lead-times and the models’ worst performances were obtained for the most complex streamflow (Ljusnan River), (2) ANN was the best model for 1-day ahead forecasting independent of complexity, (3) SETAR was the best model for 7-day ahead forecasting by means of performance indices, especially for less complexity, (4) the largest error propagation was obtained with the k-nn and ANN and thus these models should be carefully used beyond 2-day forecasting, and (5) higher number input variables except for the dominant variables made insignificant impact on forecasting performances for ANN and k-nn models.     

变截面修正Timoshenko梁自振频率的回传射线矩阵法分析

考虑剪切变形对转动惯量的影响,对经典Timoshenko梁理论进行修正,并基于回传射线矩阵法,将横截面线性变化的圆台均匀分为多段分段等截面等效梁,推导并求解三种经典边界(两端简支、两端固支、一端固支一端自由)条件下变截面修正Timoshenko梁的自振频率,进一步分析分段数目和梁长度的变化对变截面修正Timoshenko梁自振频率的影响;将计算结果与相同边界条件下经典Timoshenko梁的相应结果进行对比。研究表明：回传射线矩阵法用于分析变截面梁的自振频率时具有良好的计算精度和收敛性;相同边界条件下修正Timoshenko梁的自振频率小于经典Timoshenko梁,且梁越短粗,修正造成的影响(剪切变形对转动惯量的影响)就越大。