Prediction of extreme wave heights using neuro wavelet technique

Precise prediction of extreme wave heights is still an evading problem whether it is done using physics based modeling or by extensively used data driven technique of Artificial Neural Network (ANN). In the present paper, Neuro Wavelet Technique (NWT) is used specifically to explore the possibility of prediction of extreme events for five major hurricanes Katrina 2005, Dean 2007, Gustav 2008, Ike 2008, Irene 2011 at four locations (NDBC wave buoys stations)¹ namely; 42040, 42039, 41004, 41041 in the Gulf of Mexico. Neuro Wavelet Technique is employed by combining Discrete Wavelet Transform and Artificial Neural Networks. Discrete wavelet transform analyzes frequency of signal with respect to time at different scales. It decomposes time series into low (approximate) and high (detail) frequency components. The decomposition of approximate components (extreme events in the ocean wave series) can be carried out up to the desired multiple levels in order to provide relatively smooth varying amplitude series. This feature of wavelet transforms make it plausible for predicting extreme events with a better accuracy. In the present study third, fifth and seventh level of decompositions are used which facilitates 3 to 7 times filtering of low frequency events and seems to pay the dividend in the form of better prediction accuracy at extreme events. To develop these Neuro wavelet models to forecast the waves with lead times of 12 hr to 36 hr in advance, previously measured significant wave heights at same locations were used. The results were judged by wave plots, scatter plots and other error measures. From the results it can be concluded that the Neuro Wavelet Technique can be employed to solve the ever eluding problem of accurate forecasting of the extreme events.     

Error assessment of grid-based diffuse solar radiation models

Two grid-based diffuse solar radiation models, ESRI’s Solar Analyst (SA) and Kumar’s model (KM), were assessed using a data-independent approach where each model’s numerical results of clear sky diffuse radiation on V/U-shaped surfaces were compared with analytical results derived using each model’s assumptions. SA and KM consistently underestimate and overestimate, respectively, diffuse radiation at daily, seasonal, and annual scale relative to the analytical results based on each model’s parameterizations. Overall, SA performs better than KM in modeling diffuse radiation at most timescales. While SA and KM have similar error in calculating diffuse radiation on a horizontal surface, SA models sky view factor much better than KM, with mean absolute relative differences of 0.76% and 17.02%, respectively. KM has a large error in sky view factor as it does not consider the shading effect from surrounding terrain. Sky view factor error in SA is small and use of more zenith divisions can further reduce the error. Based on our previous study, model performance on clear sky global solar radiation was also evaluated. Overall, KM performs better than SA in global radiation as KM performs better than SA in modeling direct radiation which is the major component of global radiation.     

离散Hopfield神经网络的稳定性

研究离散Ｈｏｐｆｉｅｌｄ神经网络的串行稳定性和并行稳定性，分别提出了条件较弱的串行稳定性和并行稳定性的新判据．     

日本对虾人工越冬促熟及育苗技术的研究

本试验是利用海捕日本对虾，根据它的生活习性和生态特点，在室内水泥池内采用模仿自然条件，在不损害其生理机能的情况下，尽可能满足其性腺成熟所需要的生理生态条件的方法，即在自然水温下，采用弱光（在501X以内），雌雄比1：1，池底铺砂（铺砂面积占全面积的1/2，厚度为10～20cm）以及投喂新鲜牡蛎等进行人工越冬促熟。本试验，越冬的水温最低为14．2℃；比重为1．0215～1．0240；pH为8．0～8．5；亲虾越冬池为15m×2m×1m。其结果：越冬亲虾的存活率为85％；成熟率为74％；亲虾的产卵量为15×144～30×104粒；受精率为95％以上；孵化率为90％～95％。试验表明，不采用切除眼柄的方法，同样可达到日本对虾性腺成熟的目的；个体较大（20cm以上），其成熟率和存活率都较高，产卵量亦较多，因此应选用个体大的对虾进行越冬促熟，效果较好。本试验进行了日本对虾生产性育苗，其育苗工艺与其它虾种大同小异。育苗时，幼体的饵料应多样化；当变态进入Z2时，就兼投少量的刚孵出的卤虫幼体，能提高幼体的存活率。采用本方法进行越冬促熟，操作和管理方便，易于在生产上推广应用。     

全人工配合饵料投喂越冬亲虾试验

通过两年度的小试和生产性试验,从越冬亲虾入池至产卵过程全部投喂人工配合饵料,其成活率和性腺发育要好于投沙蚕、四角蛤的鲜活饵料组(对照组),生产性试验的产卵量、孵化率和幼体变态率优于对照组。试验用饵的成本比对照组低45.3％。     

南海珠江口海区人工放射性核素~(90)Sr,~(173)Cs分布特征的研究

一、调查区域与分析方法 珠江口调查海区是指北起虎门、南至北纬22°00′,东起大亚湾、西至东经113°30′的海域。设水质调查站20个,表层沉积物调查站14个,采集了3个生物样品。并在8个珠江入海口门处设水质和表层沉积物调查站。     

Wavelet and artificial neural network analyses of tide forecasting and supplement of tides around Taiwan and South China Sea

In multi-resolution analysis (MRA) by wavelet function Daubechies (db), we decompose the signal in two parts, the low and high-frequency contents. We remove the high-frequency content and reconstruct a new “de-noise” signal by using inverse wavelet transform. The calculation of tidal constituent phase-lags was made to determine the input and output data patterns used in building network structure of Artificial Neuron-Network (ANN) model. The “de-noise” signal was, then, used as the input data to improve the forecasting accuracy of the ANN model. The wavelet spectrum, conventional energy spectrum (fast Fourier transform, FFT), and harmonic analysis were used to analyze the characteristics of tidal data.Using only a very short-period data as a training data set in Artificial Neuron-Network Back-Propagate (ANN-BP) model, the developed ANN+Wavelet model can accurately predict or supply the missing tide data for a long period (1–5 years). The results also show that the concept of tidal constituent phase-lags can improve ANN model of tidal forecasting and data supplement. The addition of the wavelet analysis to ANN method can prominently improve the prediction quality.     

Near-shore swell estimation from a global wind-wave model: Spectral process,linear, and artificial neural network models

Estimation of swell conditions in coastal regions is important for a variety of public, government, and research applications. Driving a model of the near-shore wave transformation from an offshore global swell model such as NOAA WaveWatch3 is an economical means to arrive at swell size estimates at particular locations of interest. Recently, some work (e.g. Browne et al. [Browne, M., Strauss, D., Castelle, B., Blumenstein, M., Tomlinson, R., 2006. Local swell estimation and prediction from a global wind-wave model. IEEE Geoscience and Remote Sensing Letters 3 (4), 462–466.]) has examined an artificial neural network (ANN) based, empirical approach to wave estimation. Here, we provide a comprehensive evaluation of two data driven approaches to estimating waves near-shore (linear and ANN), and also contrast these with a more traditional spectral wave simulation model (SWAN). Performance was assessed on data gathered from a total of 17 near-shore locations, with heterogenous geography and bathymetry, around the continent of Australia over a 7 month period. It was found that the ANNs out-performed SWAN and the non-linear architecture consistently out-performed the linear method. Variability in performance and differential performance with regard to geographical location could largely be explained in terms of the underlying complexity of the local wave transformation.     

盐碱地中华绒螯蟹育苗水质调配技术的研究

研究了地下卤水中Na^ ／K^ 对中华绒螫蟹(Eriocheir sinensis)幼体生长发育的影响以及幼体对人工海水盐度的适应性。结果表明：随着地下卤水中Na^ ／K^ 的降低，中华绒螫蟹幼体存活率、变态率和体质量增长率逐渐升高，当地下卤水中的Na^ ／K^ 低于18、6时，中华绒螯蟹幼体的存活率、变态率和体质量增长率与海水对照相比差异不显著；人工海水盐度梯度设置为18～26，Z1→Z2、Z3→Z4、Z5→M各阶段不同处理组幼体存活率和体质量增长率差异显著，而变态率差异基本不显著，各阶段的最适盐度分别为20，22和20。