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.     

FLOOD STAGE FORECASTING WITH SUPPORT VECTOR MACHINES1

ABSTRACT: Machine learning techniques are finding more and more applications in the field of forecasting. A novel regression technique, called Support Vector Machine (SVM), based on the statistical learning theory is explored in this study. SVM is based on the principle of Structural Risk Minimization as opposed to the principle of Empirical Risk Minimization espoused by conventional regression techniques. The flood data at Dhaka, Bangladesh, are used in this study to demonstrate the forecasting capabilities of SVM. The result is compared with that of Artificial Neural Network (ANN) based model for one‐lead day to seven‐lead day forecasting. The improvements in maximum predicted water level errors by SVM over ANN for four‐lead day to seven‐lead day are 9.6 cm, 22.6 cm, 4.9 cm and 15.7 cm, respectively. The result shows that the prediction accuracy of SVM is at least as good as and in some cases (particularly at higher lead days) actually better than that of ANN, yet it offers advantages over many of the limitations of ANN, for example in arriving at ANN's optimal network architecture and choosing useful training set. Thus, SVM appears to be a very promising prediction tool.     

NETWORK AND SUBWATERSHED PARAMETERS EXTRACTED FROM DIGITAL ELEVATION MODELS: THE BILLS CREEK EXPERIENCE1

ABSTRACT: An automated extraction of channel network and sub-watershed characteristics from digital elevation models (DEM) is performed by model DEDNM. This model can process DEM data of limited vertical resolution representing low relief terrain. Such representations often include ill-defined drainage boundaries and indeterminate flow paths. The application watershed is an 84 km² low relief watershed in southwestern Oklahoma. The standard for validation is the network and subwatershed parameters defined by the blue line method on USGS 7.5–minute topographic maps. Evaluation of the generated and validation networks by visual comparisons shows a high degree of correlation. Comparison of selected network parameters (channel length, slope, drainage density, etc.) and of drainage network composition (bifurcation, length, slope, and area ratios) shows that, on the average, the generated parameters are within 5 percent of those derived from the validation network. The largest discrepancies were found for the channel slope values. The results of this application demonstrate that DEDNM effectively addresses network definition problems often encountered in low relief terrain and that it can generate accurate network and subwatershed parameters under those conditions.     

智能型地理信息系统（AIGIS）在城市抗震防灾规划中的应用

本文简要介绍地理信息系统GIS的发展概况、主要功能以及在国内外的应用现状.对现有的GIS软件技术加以改进,引进人工神经元网络和模糊综合评判技术,发展了一种智能型的GIS,在我国若干城市的抗震设防区划工作中应用,取得良好的效果。     

江汉平原湖区农田防护林的小气候效应研究

在区域开发中,平原湖区的林业发展潜力很大,同时,湖区农田防护林的建设,也是促进生态平衡的一项重要措施,防护林能进一步改善湖区小气候环境,尤其对保障农业生产有着相当重要的意义。通过对江汉平原湖区的农田防护林网内的小气候效应温度,湿度,风速等要素的观测数据进行研究,我们得出的结果表明：江汉平原湖区农田林网的建设,除提供工农业生产所需的林木外,随着林木的增多,林网的完善,还可调节气候,保持水土,防风固沙     

两种定性天气预报模型的对比分析

以南京１９６５～１９９４ 年４ 月平均气温作为预报量, 选取前期５００ ｈ Ｐａ 月平均高度场相关因子, 分别建立了事件概率回归预报模型和神经网络预报模型。通过对比分析发现, 在同等条件下,由于神经网络方法能更好地反映预报量与预报因子间的非线性关系, 并能有效避免采用事件概率回归方法预报建模时, 对预报因子分级造成信息损失的缺点。因此, 其拟合和预报效果明显优于传统的概率回归预报方法     

WEP的安全技术分析及对策

在对无线局域网WLAN标准IEEE80 2 .11的WEP描述的基础上 ,笔者深入研究了WEP协议的设计上和实际应用中存在的密钥序列重复使用、消息认证和完整性等严重安全漏洞 ;详细分析了标准无线局域网中WEP协议解决方案 ;提出了WEP的改进算法WEP2 ,WEP2将IV大小增加到 12 8bit,以增强抗攻击能力 ;分析了AES解决方案的可行性 ;在密钥再生与分配方面 ,提出在MAC层实现经认证的密钥交换 ,用来改善WEP的安全脆弱性等解决措施。最后 ,还对今后的研究方向作出进一步的展望     

强化环境监测管理提高环境监测地位

总结和回顾了我国环境监测工作开展近三十年来所取得的非凡成绩,阐述了目前环境监测工作同整个环境保护事业相比较,同国外环境监测相比较存在的不足,并进一步提出了以"强化环境监测管理,提高环境监测地位"为核心的几点建议,旨在使我国的环境监测工作能更好地为环境保护和经济建设服务.     

改进BP算法在煤与瓦斯突出预测中的应用

为了正确预测煤与瓦斯突出的趋势与危险性 ,基于反向BP神经网络 ,笔者提出了一种改进的BP网络模型 :为了加快BP网络的收敛速度 ,增强其跳出局部极小点的能力 ,采用了自适应变步长法和改进模拟退火法 (SA法 )相结合的方法。实际应用表明 ,该模型收敛速度快 ,准确性高 ,具有较高的可靠性和实用性 ,是一种十分有效的煤与瓦斯突出危险性预测方法。     

煤层底板采动导水破坏深度计算的神经网络方法

在综合分析影响煤层底板采动导水破坏深度因素的基础上 ,应用人工神经网络方法 ,建立了底板破坏深度的计算模型。该模型利用现场观测资料作为学习训练样本和测试样本 ,对模型的测算结果、理论计算值和实测值进行了对比分析。结果表明 :用神经网络方法计算底板破坏深度考虑的因素更加全面 ,结果更接近于实际。笔者研究的计算模型和测算方法 ,为承压水上安全采煤决策提供了科学依据。