The effectiveness of artificial recharge in combating seawater intrusion in Salalah coastal aquifer, Oman

The Salalah central sewage treatment plant has been designed to treat 20,000 m³/day at the first stage and two further stages to double the initial capacity. The plant currently (2005) treats more than 15,000 m³/day effluents to a tertiary level, and after chlorination phase, the effluents are recharged into tube wells in a line parallel to the coast. The process aims to help stabilize the seawater interface and a part to be recovered from hand-dug wells/boreholes further inland and downstream. A three-dimensional flow and solute advection transport model was developed to assess the effectiveness of the proposed recharge scheme and to track the solute transport with respect to the design system. The advection transport model predicted that in 2020 the maximum pathlines of the injection fluids would reach the abstraction wells that are located 600 m, southward of the injection bores in about 1-year travel time in the case of the no-management interference and more than that southward under management interference. The developed flow predicted the wedge of the saline intrusion in 2019 is tracked up to 2.7 and 3.4 km from the shoreline with the injection and without the injection, respectively under constant underflow. The injection scheme is effective in pushing back the saline zone front by 700 m. This study argues that the treated wastewater would help to increase the water levels at the vicinity of the injection line and to reduce the influence of saline inflows from the coast. The reclaimed sewage recharge scheme is examined in the case of the Salalah coastal aquifer using groundwater simulation, which can also be applied to other regions with similar conditions.     

Forecast of wheat yield throughout the agricultural season using optical and radar satellite images

The aim of this study is to estimate the capabilities of forecasting the yield of wheat using an artificial neural network combined with multi-temporal satellite data acquired at high spatial resolution throughout the agricultural season in the optical and/or microwave domains. Reflectance (acquired by Formosat-2, and Spot 4–5 in the green, red, and near infrared wavelength) and multi-configuration backscattering coefficients (acquired by TerraSAR-X and Radarsat-2 in the X- and C-bands, at co- (abbreviated HH and VV) and cross-polarization states (abbreviated HV and VH)) constitute the input variable of the artificial neural networks, which are trained and validated on the successively acquired images, providing yield forecast in near real-time conditions. The study is based on data collected over 32 fields of wheat distributed over a study area located in southwestern France, near Toulouse. Among the tested sensor configurations, several satellite data appear useful for the yield forecasting throughout the agricultural season (showing coefficient of determination (R²) larger than 0.60 and a root mean square error (RMSE) lower than 9.1 quintals by hectare (q ha⁻¹)): C_VH, C_HV, or the combined used of X_HH and C_HH, C_HH and C_HV, or green reflectance and C_HH. Nevertheless, the best accurate forecast (R² = 0.76 and RMSE = 7.0 q ha⁻¹) is obtained longtime before the harvest (on day 98, during the elongation of stems) using the combination of co- and cross-polarized backscattering coefficients acquired in the C-band (C_VV and C_VH). These results highlight the high interest of using synthetic aperture radar (SAR) data instead of optical ones to early forecast the yield before the harvest of wheat.     

Simultaneously simulate vertical and horizontal expansions of a future urban landscape: a case study in Wuhan,Central China

While there are extensive studies of urban 2D forms, research on the varying geometric features and spatial distribution patterns of urban 3D spaces is comparatively rare. In this paper, we propose a coupled model, known as BPANN-CBRSortCA, which is based on a back propagation artificial neural network (BPANN) and case-based reasoning technology with sort cellular automaton (CBRSortCA) to simulate future urban building heights and their spatial distribution. BPANN–CBRSortCA uses BPANN to predict the vertical extrusion of building heights and uses CBRSortCA to simulate horizontal urban expansion. The BPANN–CBRSortCA model is innovative because of its capabilities to simultaneously project urban growth in the vertical and horizontal dimensions. The proposed model also overcomes the limitations of the traditional cellular automata models that cannot simulate ‘diffused’ urban expansion. This research used Wuhan City as a case study to simulate vertical and horizontal urban expansion from 2015 to 2025. The results showed the following: (1) in the next 10 years, new build-up will mainly appear along the edge of Hongshan and Hanyang Districts or will occupy bare land in the form of ‘filling’ and (2) the tallest buildings will be mainly located to the south of East Lake in Hongshan District and on undeveloped land within the city. These simulation results can provide a reference for future urban planning.     

神经网络模型预报湖北汛期降水量的应用研究

使用人工神经网络方法建立了湖北省汛期 (6～ 8月 )总降水量的短期气候预测模型 ,该神经网络模型的输入是汛期前期 (2～ 4月 )的北半球月平均 5 0 0 h Pa高度场、海平面气压场和太平洋海温场的扩展自然正交展开 (EEOF)的前几个主要模态的时间系数 ,输出了湖北汛期降水场的自然正交展开 (EOF)的前 2个主要模态的时间系数。41 a历史资料的交叉检验表明 :样本试验的预报技巧评分平均为 0 .2 4 6 ,虽然该模型对各年的预报效果仍存在一定的不稳定性 ,但它可为湖北汛期降水的短期气候预测提供一种具有明显统计预报正技巧的预报方法     

变形岩石磁性各向异性及其应变相关性的模拟

本文介绍处于弱磁场中的磁性砂岩人工样品,在三种应变作用下磁化率各向异性特征的研究结果.结果表明,磁化率椭球与应变椭球之间,显示了密切的相关性.文中并对上述情况下,磁化率各向异性特征及剩磁特征,从机理上进行了分析.     

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％。