SOIL SALINITY PREDICTION USING ARTIFICIAL NEURAL NETWORKS1

ABSTRACT: This study explores the applicability of Artificial Neural Networks (ANNs) for predicting salt build‐up in the crop root zone. ANN models were developed with salinity data from field lysimeters subirrigated with brackish water. Different ANN architectures were explored by varying the number of processing elements (PEs) (from 1 to 30) for replicate data from a 0.4 m water table, 0.8 m water table, and both 0.4 and 0.8 m water table lysimeters. Different ANN models were developed by using individual replicate treatment values as well as the mean value for each treatment. For replicate data, the models with twenty, seven, and six PEs were found to be the best for the water tables at 0.4 m, 0.8 m and both water tables combined, respectively. The correlation coefficients between observed salinity and ANN predicted salinity of the test data with these models were 0.89, 0.91, and 0.89, respectively. The performance of the ANNs developed using mean salinity values of the replicates was found to be similar to those with replicate data. Not only was there agreement between observed and ANN predicted salinity values, the results clearly indicated the potential use of ANN models for predicting salt build‐up in soil profile at a specific site.     

WHAT DO WE KNOW ABOUT SALT WATER INTRUSION?1

Almost thirty years after the first known publication on salt water problems published in 1855 by Braithwaite, two investigators developed an approximate theory to find the boundaries of fresh water lenses in coastal aquifers. Their theory is now known as Ghyben-Herzberg Theory. Although their theory is based on oversimplified assumptions, it has stimulated others through various periods of time. A review of the main investigations in this field is summarized in this paper. The discussions are subdivided into three main eras: (1) The period through which the problems were identified and field observations were explained (from 1855 until the early forties); (2) the analytic approaches during the period from 1940 to the late sixties; and (3) the refined techniques during the past three years. A brief summary of the recharge methods is also given in addition to a more or less comprehensive list of references.     

长江流域的鱼类资源及其保护对策

长江水系的鱼类约有300种,其中鲤科鱼类占半数以上,主要的经济鱼类多数属于鲤科。对不同江段的鱼类资源作了简介,并提出了相应的保护措施。上游江段以维护生物多样性,保护特有种为主,需要建立鱼类自然保护区;中游应保证主要经济鱼类的自然繁殖条件,加强珍稀鱼类的人工繁殖放流工作,同时对湖泊幼鱼资源进行保护;下游应注意保持江水质量,严格遵守工业废水排放标准;河口江段应规定幼鲟保护期,建议每年6月15日至7月31日停止一切损害幼鲟资源的渔捞作业。     

长江河口越江工程在上海港建设中的重要作用

探讨了连接上海，崇明和江苏海门的长江河口桥隧工程在连接河口岛屿间交通，发挥崇明的区位优势，促进地区经济发展以及在上海港建设中的作用。详细论述了越江工程完成后上海新港的扩建，根据河口河水岸线的分布，提出了在横沙岛东滩修建人工岛的设想，最后对几种越江方案进行了分析比较，提出了作者的看法。     

人工神经网络方法在资源与环境预测方面的应用

用人工神经网络方法对不同水域、不同环境因子之间非线性和不确定性的复杂关系进行学习训练并预测检验。结果表明：人工神经网络方法在模拟和预测方面 优于传统的统计回归模型,在资源与环境方面的应用是可行的。具有较强的模拟预测能力。与传统的回归模型相比,人工神经网络方法不要求监测数据具有很强的规律性,就可用后的网络模型对其进行预报,燕且预测相对误差均比回归模型预测相对误差要小,具有一定的实用性。两个实例的应用     

三峡茅坪溪沥青混凝土心墙堆石坝位移反演分析与安全预测

本文研究了基于综合应用人工神经网络和演化算法的位移反演分析方法,并将此方法应用于三峡茅坪溪沥青混凝土心墙堆石坝的变形反演分析中。以茅坪溪一期工程原型观测成果为依据,反演出能够正确反映坝体变形特性的邓肯-张EB模型参数,从而预测了施工期末和蓄水期末该坝的变形特性,并基于总应力法研究了心墙水力劈裂破坏发生的可能性,为茅坪溪堆石坝运行期的安全生产提供依据。研究结果表明,茅坪溪心墙堆石坝不会发生水力劈裂破坏,该坝蓄水后虽然水压上升,但心墙仍是安全的。     

多因素耦合条件下硫化矿自燃神经网络动态预测模型研究

硫化矿石自燃是多种因素、多场耦合综合作用的结果,是一典型的非线性问题。笔者应用人工神经网络技术,以Matlab软件为平台,通过现场调查和理论分析,建立了矿石含硫量、通风强度、环境温度3因素与硫化矿石自燃之间的预测模型;通过数据样本学习与部分现场监测数据相结合进行模拟,研究表明预测数据与实测结果基本吻合,误差控制在10%以内,取得了较好的效果。该研究为预防硫化矿石自燃提供一个新的思路和方法,具有一定的理论意义和应用价值。     

人工神经网络在水环境质量评价中的应用

为了将人工神经网络应用于水环境质量评价，应用了人工神经网络Ｂ—Ｐ算法，构造了水环境质量评价模型，该模型应用于实例评价结果表明，人工神经网络用于环境质量评价具有客观性，通用性和实用性。     

基于神经网络的洪水预报研究

人工神经网络通过神经元之间的相互作用来完成整个网络的信息处理,具有自学习和自适应等一系列优点,因而用它来进行洪水预报是可行的.对洪水预报问题,初步建立了基于神经网络的洪水预报系统,给出了应用实例.     

人工湿地污水处理工艺及除污机理

对人工湿地污水处理工艺及其对水中污染物的去除机理进行了综述，并对人工湿地处理污水的技术进行了展望。