Decentralized Groundwater Recharge Systems Using Roofwater and Stormwater Runoff1

Stephens, Daniel B., Mark Miller, Stephanie J. Moore, Todd Umstot, and Deborah J. Salvato, 2011. Decentralized Groundwater Recharge Systems Using Roofwater and Stormwater Runoff. Journal of the American Water Resources Association (JAWRA) 48(1): 134‐144. DOI: 10.1111/j.1752‐1688.2011.00600.x Abstract: Stormwater capture for groundwater recharge in urban areas is usually conducted at the regional level by water agencies. Field and modeling studies in New Mexico indicate that stormwater diverted to retention basins may recharge about 50% of precipitation that falls on the developed area, even in dry climates. Comparable volumes of recharge may be expected at homes, subdivisions, or commercial properties with low‐impact development (LID) technologies for stormwater control that promote recharge over evapotranspiration. Groundwater quality has not been significantly impacted at sites that have been recharging stormwater to aquifers for decades. Distributed recharge systems may be a good alternative to centralized regional facilities where there is limited land for constructing spreading basins or little funding for new infrastructure. LID technologies borrowed from stormwater managers are important tools for groundwater managers to consider to enhance recharge.     

Energy management in hybrid electric vehicles using optimized radial basis function neural network

This paper deals with energy management in hybrid electric vehicles. Use of radial basis function neural network (RBFNN) for the problem of energy management gains importance in the present decade. Use of genetic algorithm (GA) and particle swarm optimization (PSO) as optimization algorithms for parameter estimation is also well known. However, none of the researchers in the area tried to use GA and PSO as training algorithms for the problem. Hence in this paper, we propose two novel methods, based on RBFNN. The difference between RBFNN-based approaches in the literature and those used in this paper is the use of GA and PSO (i.e. optimising algorithms) as training algorithm to train RBFNNs. Interestingly, it is seen that the proposed approaches of this paper outperform RBFNN-based approaches in the literature with traditional training.     

Comparison of ANN,MVR, and SWR models for computing thermal efficiency of a solar still

In this paper, the viability of modeling the instantaneous thermal efficiency (η_ith) of a solar still was determined using meteorological and operational data with an artificial neural network (ANN), multivariate regression (MVR), and stepwise regression (SWR). This study used meteorological and operational variables to hypothesize the effect of solar still performance. In the ANN model, nine variables were used as input parameters: Julian day, ambient temperature, relative humidity, wind speed, solar radiation, feed water temperature, brine water temperature, total dissolved solids of feed water, and total dissolved solids of brine water. The η_ith was represented by one node in the output layer. The same parameters were used in the MVR and SWR models. The advantages and disadvantages were discussed to provide different points of view for the models. The performance evaluation criteria indicated that the ANN model was better than the MVR and SWR models. The mean coefficient of determination for the ANN model was about 13% and14% more accurate than those of the MVR and SWR models, respectively. In addition, the mean root mean square error values of 6.534% and 6.589% for the MVR and SWR models, respectively, were almost double that of the mean values for the ANN model. Although both MVR and SWR models provided similar results, those for the MVR were comparatively better. The relative errors of predicted η_ith values for the ANN model were mostly in the vicinity of ±10%. Consequently, the use of the ANN model is preferred, due to its high precision in predicting η_ith compared to the MVR and SWR models. This study should be extremely beneficial to those coping with the design of solar stills.     

基于改进型灰色神经网络组合模型的空气质量预测

基于空气质量数据不足及波动较大的情况,将灰色GM（1,1）模型与人工神经网络模型组合并改进,建立改进型灰色神经网络组合模型。利用天津市2001—2008年PM10、SO2和NO2年均值作为原始数据预测2009—2010年PM10、SO2和NO2的浓度以进行模型精度检验,最后利用该模型预测2011—2015年天津市空气质量状况。结果表明,与灰色GM（1,1）模型、传统灰色神经网络组合模型相比,所建立的改进型灰色神经网络组合模型相对模拟误差小,预测结果更为可靠,可以用于空气质量预测。     

广州市白云人工湖生态修复工程设计

广州市白云湖是广州市最大的人工生态服务湖泊,通过对白云湖生态修复工程进行设计,采用了炭素纤维生态草、微纳米气泡发生装置及纯氧发生器和人工干预与生物处理相结合生态浮床等环保新技术对白云湖水体进行综合生态修复,并对白云湖水质进行跟踪监测。结果显示,白云湖水质提升工程实施之后,白云湖各项水质指标都有一定的改善。白云湖生态修复工程的设计经验对于类似人工水体的水质改善和生态修复工程建设具有参考意义。     

基于人工神经网络和遗传算法研究锰过氧化酶脱色甲基橙

基于锰过氧化物酶(MnP)氧化脱色偶氮类染料的原理,实验研究MnP对甲基橙的脱色工艺,采用人工神经网络(ANN)和遗传算法(GA)建立脱色模型并优化工艺。建立的ANN模型的误差、相关系数、均方根误差和绝对平均偏差分别为0.0009、0.9971、1.21和6.82,模型有效且能够用于预测和工艺优化。采用GA对ANN模型进行数值寻优,得到的最佳工艺条件为酶液量0.6 mL,Mn2+浓度4 mmol/L,H2O2浓度0.49 mmol/L。该条件下脱色率达到(90.74±0.59)%。ANN耦合GA有效地建立了锰过氧化物酶脱色甲基橙的模型,并优化了工艺参数,为甲基橙脱色的研究提供一定参考。     

两类新型神经网络及其在安全评价中的应用

详细讨论小波神经网络(WNN)和模糊神经网络(FNN)的构造以及训练学习过程,并针对安全评价问题分别完成算例。典型算例表明:小波神经网络具有很好的逼近与映射能力,并且有很强的泛化能力;模糊神经网络将模糊数学与人工神经网络相互融合起来,有效提升了系统的智能功能。两类新型神经网络使得人-机-环境系统工程中的许多安全评价问题有了更广泛的量化工具,并具有安全评价的量化较准确的特点。     

秦皇岛地区气候干旱与人工增雨的探索

气候分析表明:随着气候变化及环境城市化,秦皇岛市区域年平均气温指数呈上升趋势,特别是近10年出现了连续偏高的异常变化;年降水指数呈明显减小趋势,出现了连年干旱,高温少雨不仅使生态环境不断恶化,而且使农田干旱、水库蓄水严重不足,因此人工增雨变得更加迫切。目前,秦皇岛市人工增雨工作已进入常态化、业务化轨道。总结1999—2008年火箭人工增雨经验,人工增雨操作的技术关键:一是分析局地降水气候规律;二是利用碘化银进行人工增雨需要适当的温度条件,即所谓进行冷云催化。在增雨时应充分考虑这一物理机制,选择好合适的发射高度,把催化剂送到可催化的温度环境中,以保证作业成功率。     

A hybrid model based on smooth transition periodic autoregressive and Elman artificial neural network for wind speed forecasting of the Hebei region in China

Wind energy, one of the most promising renewable and clean energy sources, is becoming increasingly significant for sustainable energy development and environmental protection. Given the relationship between wind power and wind speed, precise prediction of wind speed for wind energy estimation and wind power generation is important. For proper and efficient evaluation of wind speed, a smooth transition periodic autoregressive (STPAR) model is developed to predict the six-hourly wind speeds. In addition, the Elman artificial neural network (EANN)-based error correction technique has also been integrated into the new STPAR model to improve model performance. To verify the developed approach, the six-hourly wind speed series during the period of 2000–2009 in the Hebei region of China is used for model construction and model testing. The proposed EANN-STPAR hybrid model has demonstrated its powerful forecasting capacity for wind speed series with complicated characteristics of linearity, seasonality and nonlinearity, which indicates that the proposed hybrid model is notably efficient and practical for wind speed forecasting, especially for the Hebei wind farms of China.     

Classifying environmentally significant urban land uses with satellite imagery

We investigated Bayesian networks to classify urban land use from satellite imagery. Landsat Enhanced Thematic Mapper Plus (ETM(+)) images were used for the classification in two study areas: (1) Marina del Rey and its vicinity in the Santa Monica Bay Watershed, CA and (2) drainage basins adjacent to the Sweetwater Reservoir in San Diego, CA. Bayesian networks provided 80-95% classification accuracy for urban land use using four different classification systems. The classifications were robust with small training data sets with normal and reduced radiometric resolution. The networks needed only 5% of the total data (i.e., 1500 pixels) for sample size and only 5- or 6-bit information for accurate classification. The network explicitly showed the relationship among variables from its structure and was also capable of utilizing information from non-spectral data. The classification can be used to provide timely and inexpensive land use information over large areas for environmental purposes such as estimating stormwater pollutant loads.