Integration of transfer function model and back propagation neural network for forecasting storm sewer flow in Taipei metropolis

Typhoons and storms have often brought heavy rainfalls and induced floods that have frequently caused severe damage and loss of life in Taiwan. Our ability to predict sewer discharge and forecast floods in advance during storm seasons plays an important role in flood warning and flood hazard mitigation. In this paper, we develop an integrated model (TFMBPN) for forecasting sewer discharge that combines two traditional models: a transfer function model and a back propagation neural network. We evaluated the integrated model and the two traditional models by applying them to a sewer system of Taipei metropolis during three past typhoon events (NARI, SINLAKU, and NAKR). The performances of the models were evaluated by using predictions of a total of 6 h of sewer flow stages, and six different evaluation indices of the predictions. Finally, an overall performance index was determined to assess the overall performance of each model. Based on these evaluation indices, our analysis shows that TFMBNP yields accurate results that surpass the two traditional models. Thus, TFMBNP appears to be a promising tool for flood forecasting for the Taipei metropolis sewer system. For publication in Stochastic Environmental Research and Risk Analysis.     

A geostatistically based inverse model for three-dimensional variably saturated flow

We present a geostatistically based inverse model for characterizing heterogeneity in parameters of unsaturated hydraulic conductivity for three-dimensional flow. Pressure and moisture content are related to perturbations in hydraulic parameters through cross-covariances, which are calculated to first-order. Sensitivities needed for covariance calculations are derived using the adjoint state sensitivity method. Approximations of the conditional mean parameter fields are then obtained from the cokriging estimator. Correlation between parameters and pressure – moisture content perturbations is seen to be strongly dependent on mean pressure or moisture content. High correlation between parameters and pressure data was obtained under saturated or near saturated flow conditions, providing accurate estimation of saturated hydraulic conductivity, while moisture content measurements provided accurate estimation of the pore size distribution parameter under unsaturated flow conditions.     

一个与铬铁矿富集有关的巨型实验

现在岩石学、矿床学的实验一般用样品是几十毫克、甚至十几毫克,其目的是要精确化。但是许多事实说明,这种精密的实验工作并不能完全解决矿床的成因。要解决矿床成因的问题,至少必须回答下面三个问题:(1)有用金属矿物是在什么条件下形成的;(2)有用金属矿物是如何富集为矿石的;(3)矿石如何具有一定的量,并达到经济上可开采的要求。实验岩石学和实验矿床学的资料一般只说明了第一个问题。     

沉积矿床成矿时代的地史意义

地质工作者研究矿床的意义,不仅在于认识矿床的形成与组成,不仅在于说明矿床的存在状况,还在于深入矿床的本质,指出找矿预测的途径;结合生产斗争实践,为社会主义革命和社会主义建设服务。     

CaMnSi2O6-CaAlSiAlO6系统的实验研究

冶金炉渣是在迅速冷却的条件下结晶的,铸石是在熔体的固相线以下恒温结晶的,这两种情况皆属不平衡状态。过去的硅酸盐体系的实验研究几乎仅局限于平衡状态,对非平衡状态研究甚少,只是近来才开始这方面的研究。在硅锰渣铸石和硅锰渣微晶铸石中,主要矿物为锰铸普通辉石--钙锰辉石与钙契尔马克分子（CaAlSiAlO₆）以及少量的Mg₂Si₂O₆的固溶体,其次是钙蔷薇辉石和钙长石,另外还有极少量的方锰石、硫化锰等。     

The role of anthropogenic activities in karst spring discharge volatility

The traditional hydrological time series methods tend to focus on the mean of whichever variable is analysed but neglect its time‐varying variance (i.e. assuming the variance remains constant). The variances of hydrological time series vary with time under anthropogenic influence. There is evidence that extensive well drilling and groundwater pumping can intercept groundwater run‐off and consequently induce spring discharge volatility or variance varying with time (i.e. heteroskedasticity). To investigate the time‐varying variance or heteroskedasticity of spring discharge, this paper presents a seasonal autoregressive integrated moving average with general autoregressive conditional heteroskedasticity (SARIMA‐GARCH) model, whose the SARIMA model is used to estimate the mean of hydrological time series, and the GARCH model estimates its time‐varying variance. The SARIMA‐GARCH model was then applied to the Xin'an Springs Basin, China, where extensive groundwater development has occurred since 1978 (e.g. the average annual groundwater pumping rates were less than 0.20 m³/s in the 1970s, reached 1.20 m³/s at the end of the 1980s, surpassed 2.0 m³/s in the 1990s and exceeded 3.0 m³/s by 2007). To identify whether human activities or natural stressors caused the heteroskedasticity of Xin'an Springs discharge, we segmented the spring discharge sequence into two periods: a predevelopment stage (i.e. 1956–1977) and a developed stage (i.e. 1978–2012), and set up the SARIMA‐GARCH model for the two stages, respectively. By comparing the models, we detected the role of human activities in spring discharge volatility. The results showed that human activities caused the heteroskedasticity of the Xin'an Spring discharge. The predicted Xin'an Springs discharge by the SARIMA‐GARCH model showed that the mean monthly spring discharge is predicted to continue to decline to 0.93 m³/s in 2013, 0.67 m³/s in 2014 and 0.73 m³/s in 2015. Copyright © 2014 John Wiley & Sons, Ltd.     

A new solution for a partially penetrating constant‐rate pumping well with a finite‐thickness skin

A mathematical model describing the constant pumping is developed for a partially penetrating well in a heterogeneous aquifer system. The Laplace‐domain solution for the model is derived by applying the Laplace transforms with respect to time and the finite Fourier cosine transforms with respect to vertical co‐ordinates. This solution is used to produce the curves of dimensionless drawdown versus dimensionless time to investigate the influences of the patch zone and well partial penetration on the drawdown distributions. The results show that the dimensionless drawdown depends on the hydraulic properties of the patch and formation zones. The effect of a partially penetrating well on the drawdown with a negative patch zone is larger than that with a positive patch zone. For a single‐zone aquifer case, neglecting the effect of a well radius will give significant error in estimating dimensionless drawdown, especially when dimensionless distance is small. The dimensionless drawdown curves for cases with and without considering the well radius approach the Hantush equation (Advances in Hydroscience. Academic Press: New York, 1964) at large time and/or large distance away from a test well. Copyright © 2007 John Wiley & Sons, Ltd.     

Analysis of karst aquifer spring flows with a gray system decomposition model

There are approximately 470,000 km(2) of karst aquifers that feed many large springs in North China. Turbulent flow often exists in these karst aquifers, which means that the classical ground water model based on Darcy's law cannot be applied here. Ground water data are rare for these aquifers. As a consequence, it is difficult to quantitatively investigate ground water flow in these karst systems. The purpose of this study is to develop a parsimonious model that predicts karst spring discharge using gray system theory. In this theory, a white color denotes a system that is completely characterized and a black color represents a system that is totally unknown. A gray system thus describes a complex system whose characteristics are only partially known or known with uncertainty. Using this theory, we investigated the karst spring discharge time series over different time scales. First, we identified three specific components of spring discharge: the long-term trend, periodic variation, and random fluctuation. We then used the gray system model to simulate the long-term trend and obtain periodic variation and random fluctuation components. Subsequently, we developed a predictive model for karst spring discharge. Application of the model to Liulin Springs, a representative example of karst springs in northern China, shows that the model performs well. The predicted results suggest that the Liulin Springs discharge will likely decrease over time, with small fluctuations.