Locally weighted learning based hybrid intelligence models for groundwater potential mapping and modeling: A case study at Gia Lai province, Vietnam

The groundwater potential map is an important tool for a sustainable water management and land use planning,particularly for agricultural countries like Vietnam.In this article,we proposed new machine learning ensemble techniques namely AdaBoost ensemble(ABLWL),Bagging ensemble(BLWL),Multi Boost ensemble(MBLWL),Rotation Forest ensemble(RFLWL)with Locally Weighted Learning(LWL)algorithm as a base classifier to build the groundwater potential map of Gia Lai province in Vietnam.For this study,eleven conditioning factors(aspect,altitude,curvature,slope,Stream Transport Index(STI),Topographic Wetness Index(TWI),soil,geology,river density,rainfall,land-use)and 134 wells yield data was used to create training(70%)and testing(30%)datasets for the development and validation of the models.Several statistical indices were used namely Positive Predictive Value(PPV),Negative Predictive Value(NPV),Sensitivity(SST),Specificity(SPF),Accuracy(ACC),Kappa,and Receiver Operating Characteristics(ROC)curve to validate and compare performance of models.Results show that performance of all the models is good to very good(AUC:0.75 to 0.829)but the ABLWL model with AUC=0.89 is the best.All the models applied in this study can support decision-makers to streamline the management of the groundwater and to develop economy not only of specific territories but also in other regions across the world with minor changes of the input parameters.

Modeling of Spatial Distributions of Farmland Density and Its Temporal Change Using Geographically Weighted Regression Model

This study used spatial autoregression(SAR)model and geographically weighted regression(GWR)model to model the spatial patterns of farmland density and its temporal change in Gucheng County,Hubei Province,China in 1999 and 2009,and discussed the difference between global and local spatial autocorrelations in terms of spatial heterogeneity and non-stationarity.Results showed that strong spatial positive correlations existed in the spatial distributions of farmland density,its temporal change and the driving factors,and the coefficients of spatial autocorrelations decreased as the spatial lag distance increased.SAR models revealed the global spatial relations between dependent and independent variables,while the GWR model showed the spatially varying fitting degree and local weighting coefficients of driving factors and farmland indices(i.e.,farmland density and temporal change).The GWR model has smooth process when constructing the farmland spatial model.The coefficients of GWR model can show the accurate influence degrees of different driving factors on the farmland at different geographical locations.The performance indices of GWR model showed that GWR model produced more accurate simulation results than other models at different times,and the improvement precision of GWR model was obvious.The global and local farmland models used in this study showed different characteristics in the spatial distributions of farmland indices at different scales,which may provide the theoretical basis for farmland protection from the influence of different driving factors.

Experimental Investigation and Development of Artificial Neural Network Model for the Properties of Locally Produced Light Weight Aggregate Concrete

The developments in the field of construction raise the need for concrete with less weight. This is beneficial for different applications starting from the less load applied to foundations and soil till the reduction of carnage capacity required for lifting precast units. In this paper, the production of light weight concrete from light local weight aggregate is investigated. Three candidate materials are used: crushed fired brick, vermiculite and light exfoliated clay aggregate (LECA). The first is available as the by-product of brick industry and the later two types are produced locally for different applications. Nine concrete mixes were made with same proportions and different aggregate materials. Physical and mechanical properties were measured for concrete in fresh and hardened states. Among these measured ones are unit weight, slump, compressive and tensile strength, and impact resistance. Also, the performance under elevated temperature was measured. Results show that reduction of unit weight up to 45%, of traditional concrete, can be achieved with 50% reduction in compressive strength. This makes it possible to get structural light weight concrete with compressive strength of 130 kg/cm2. Light weight concrete proved also to be more impact and fire resistant. However, as expected, it needs separate calibration curves for non-destructive evaluation. Following this experimental effort, the Artificial Neural Network (ANN) technique was applied for simulating and predicting the physical and mechanical properties of light weight aggregate concrete in fresh and hardened states. The current paper introduced the (ANN) technique to investigate the effect of light local weight aggregate on the performance of the produced light weight concrete. The results of this study showed that the ANN method with less effort was very efficiently capable of simulating the effect of different aggregate materials on the performance of light weight concrete.

FUNCTIONAL-COEFFICIENT REGRESSION MODEL AND ITS ESTIMATION

In this paper,a class of functional-coefficient regression models is proposed and an estimation procedure based on the locally weighted least equares is suggested.This class of models,with the proposed estimation method,is a powerful means for exploratory data analysis.

Optimization of multi-model ensemble forecasting of typhoon waves

Accurately forecasting ocean waves during typhoon events is extremely important in aiding the mitigation and minimization of their potential damage to the coastal infrastructure, and the protection of coastal communities. However, due to the complex hydrological and meteorological interaction and uncertainties arising from different modeling systems, quantifying the uncertainties and improving the forecasting accuracy of modeled typhoon-induced waves remain challenging. This paper presents a practical approach to optimizing model-ensemble wave heights in an attempt to improve the accuracy of real-time typhoon wave forecasting. A locally weighted learning algorithm is used to obtain the weights for the wave heights computed by the WAVEWATCH III wave model driven by winds from four different weather models （model-ensembles）. The optimized weights are subsequently used to calculate the resulting wave heights from the model-ensembles. The results show that the opti- mization is capable of capturing the different behavioral effects of the different weather models on wave generation. Comparison with the measurements at the selected wave buoy locations shows that the optimized weights, obtained through a training process, can significantly improve the accuracy of the forecasted wave heights over the standard mean values, particularly for typhoon-induced peak waves. The results also indicate that the algorithm is easy to imnlement and practieal for real-time wave forecasting.

ADDITIVE HAZARDS MODEL WITH TIME-VARYING REGRESSION COEFFICIENTS

This article discusses regression analysis of failure time under the additive hazards model, when the regression coefficients are time-varying. The regression coefficients are estimated locally based on the pseudo-score function [12] in a window around each time point. The proposed method can be easily implemented, and the resulting estimators are shown to be consistent and asymptotically normal with easily estimated variances. The simulation studies show that our estimation procedure is reliable and useful.

Concentration Prediction of Total Flavonoids in Aurantii Fructus Extraction Process:Locally Weighted Regression versus Kinetic Model Equation Based on Fick's Law

Objective To predict the total flavonoids concentration of Aurantii Fructus fried with bran in its extraction process. Methods Ultraviolet spectrophotometry was used to determine the concentration of total flavonoids in different extraction time （t） and solvent load （M）. Then the predicted procedure was carried out using the following data： 1 ） based on Ficks second law, the parameters of the kinetic model could be deduced and the equation was established; 2） Locally weighted regression （LWR） code was developed in the WEKA software environment to predict the concentration. And then we used both methods to predict the concentration of total flavonoids in new experiments. Results After comparing the predicted results with the experimental data, the LWR model had better accuracy and performance in the prediction. Conclusion LWR is applied to analyze the extraction process of Chinese herb for the first time, and it is totally fit for the extraction. LWR-based system is a more simple and accurate way to predict than the established equation. It is a good choice especially for a process which exists no clear rules, and can be used in the real-time control during the process.

STATISTICAL INFERENCES FOR VARYING-COEFFICINT MODELS BASED ON LOCALLY WEIGHTED REGRESSION TECHNIQUE

Some fundamental issues on statistical inferences relating to varying-coefficient regression models are addressed and studied. An exact testing procedure is proposed for checking the goodness of fit of a varying-coefficient model fited by the locally weighted regression technique versus an ordinary linear regression model. Also, an appropriate statistic for testing variation of model parameters over the locations where the observations are collected is constructed and a formal testing approach which is essential to exploring spatial non-stationarity in geography science is suggested.

多工况生产过程下的即时学习能耗预测建模方法

针对全局能耗预测模型只适用于部分预测样本且模型计算量大的问题,引入即时学习思想,采用局部加权偏最小二乘法结合能耗模型建立临时局部能耗预测模型;改进粒子群算法的惯性权重,考虑粒子适应度、迭代次数和种群大小对粒子群算法收敛速度和收敛精度的影响,提出一种非线性变化的自适应惯性权重策略,离线计算阶段使用改进的粒子群算法(adaptive PSO,APSO)对历史样本的带宽参数进行寻优,当预测样本到来时在线更新局部模型。考虑多工况生产场景下不同工况样本之间的能耗差异性所导致的预测误差,增加工况相似性度量过程,提出局部加权偏最小二乘算法与K-means算法相结合的APSO-JITL(just-in-time learning)-CLWPLS(cluster locally weighted partial least squares)能耗预测建模方法,在预测时选取同一工况的历史样本来设计预测样本的带宽参数。通过仿真实验验证了算法有着更高的预测精度且能更好地应对多工况生产场景。

基于多应用场景的改进DV-Hop定位模型

针对距离矢量跳(DV-Hop)定位模型定位精度低、优化策略场景依赖性强的问题,提出一种基于函数分析和模拟定参的改进DV-Hop模型——函数修正距离矢量跳(FuncDV-Hop)定位模型。首先,分析DV-Hop模型的平均跳距、距离估计和最小二乘法中的误差原因,引入待定系数优化、阶跃函数分段实验、带等效点的权重函数策略和极大似然估计修正;其次,考虑多应用场景,用控制变量法,分别将总节点数、信标节点比例、通信半径、信标节点数和待测节点数作为变量,设计对照实验;最后,进行仿真定参和整合优化测试两阶段实验,最终的改进策略较原DV-Hop模型的定位精度提高了23.70%~75.76%,平均优化率57.23%。实验结果表明,FuncDV-Hop模型的优化率最高达到了50.73%,与基于遗传算法和神经动力学改进的DV-Hop模型相比,FuncDV-Hop模型的优化率提升了0.55%~18.77%。所提模型不引入其他参量,不增加无线传感器网络(WSN)的协议开销,且有效提高定位精度。

异方差混合地理加权回归模型的正交投影估计

文章针对误差项存在空间异方差的混合地理加权回归模型,提出了一种新的估计方法。该方法将正交投影、局部线性估计和广义最小二乘估计的思想相结合,能够单独对模型中的常数系数、系数函数和方差函数进行估计。通过数值模拟对所提方法的性能进行验证。模拟结果表明,所提方法比现有的再加权估计方法更具优势。最后,基于城镇居民文化消费水平及其影响因素的实证分析验证了所提方法的实用性。

基于扩散模型生成的高b值DWI评估前列腺癌根治性治疗后局部复发的应用价值

目的探讨基于扩散模型生成高b值扩散加权成像(diffusion weighted imaging,DWI)对前列腺癌根治性治疗后局部复发的评估价值。材料与方法回顾性分析63例前列腺癌行根治性放射治疗(radiation therapy,RT)或根治性前列腺切除术(radical prostatectomy,RP)后出现生化复发(biochemical recurrence,BCR)患者的临床及影像相关资料,其中RT组21例,RP组42例。将患者初始表观扩散系数(apparent diffusion coefficient,ADC)图经过计算得到的DWI图像输入前列腺DWI生成模型,获得生成的高b值(b=2000 s/mm^(2))DWI图。通过3位阅片者对计算DWI及生成DWI进行图像质量评价,并根据前列腺复发影像报告(Prostate Imaging for Recurrence Reporting,PI-RR)系统对所有病例进行复发风险评分采用多读者多病例受试者工作特征(multi-reader multi-case receiver operating characteristic,MRMC-ROC)曲线比较不同阅片者的诊断效能差异。等级评分一致性采用组内相关系数进行检验。结果3位阅片者对生成DWI组图像质量评价均优于计算DWI组(P=0.002、0.003、0.002)。3位阅片者对RT组的生成DWI与计算DWI组的PI-RR总评分差异有统计学意义(P=0.031、0.049、0.041);3位阅片者对RP组生成DWI与计算DWI组的PI-RR总评分差异有统计学意义(P=0.034、0.049、0.036)。3位阅片者使用生成DWI进行RT与RP两组PI-RR总评分预测发生局部复发的曲线下面积(area under the curve,AUC)值范围分别为0.884~0.924、0.926~0.947;利用计算DWI进行RT与RP两组PI-RR总评分预测发生局部复发的AUC值范围分别为0.783~0.792、0.843~0.893。合并RT及RP两组病例后,使用PI-RR总评分预测所有患者局部复发的状态,生成DWI组与计算DWI组的AUC值范围分别为0.912~0.930、0.797~0.858。结论基于扩散模型生成的高b值DWI能显著提高前列腺癌根治性治疗后局部复发诊断效能。

基于特征点动态选择的三维人脸点云模型重建

针对典型的点云配准方法中伪特征点过多导致配准效率低和配准结果不精确的问题,提出一种基于特征点动态选择的三维人脸点云模型重建方法。该方法在粗配准阶段,采用动态特征矩阵求解法获取粗匹配特征变换矩阵以避免伪特征点的干扰。在精配准过程中,采用二次加权法向量垂直距离法在人脸流形表面选择更有效的特征点以减少伪特征点的数量,并采用基于特征融合与局部特征一致性的迭代最近点方法进行精配准。经过对比实验验证了算法的可行性,实验结果表明,该算法能够实现高精度且快速的三维人脸点云模型重建,且均方根误差达到1.8165 mm,相较其他算法,其在模型重建精度和效率方面都有所提升,具有良好的应用前景。

Quality-related locally weighted soft sensing for non-

Soft sensors are widely used to predict quality variables which are usually hard to measure.It is necessary to construct an adaptive model to cope with process non-stationaries.In this study,a novel quality-related locally weighted soft sensing method is designed for non-stationary processes based on a Bayesian network with latent variables.Specifically,a supervised Bayesian network is proposed where quality-oriented latent variables are extracted and further applied to a double-layer similarity meas-urement algorithm.The proposed soft sensing method tries to find a general approach for non-stationary processes via quality-related information where the concepts of local similarities and window confidence are explained in detail.The performance of the developed method is demonstrated by application to a numerical example and a debutanizer column.It is shown that the proposed method outperforms competitive methods in terms of the accuracy of predicting key quality variables.

基于SVM与局部加权的KNN算法的研究与实现

文章主要研究基于SVM与局部加权的KNN算法。在研究过程中,首先分析了KNN算法的基本概念和实现原理。在此基础上,引入SVM模型与局部加权方法对KNN算法进行改进,并将改进后的算法应用于乳腺癌识别的实例研究中。最后,在搭建的理论框架上进行了仿真实验。实验结果表明,与传统KNN算法相比,基于SVM与局部加权的KNN算法通过引入权重机制和SVM模型,在分类性能方面表现出更高的精准度,有效弥补了传统KNN算法在分类性能上的不足,显著提升了目标分类的精度。

加权小世界网络模型在知识共享中的应用研究

在对知识共享的影响因素进行深入分析的基础上,将连接边的权重引入知识共享网络.在对该网络的描述与分析中引入了近年来国外发展很快的“小世界”理论,提出用加权小世界网络模型的全局效率、局部效率和成本三个参数,来测量知识共享效果的思想.为在全局和局部范围内,创建高效的低成本知识共享网络,并进一步研究知识管理,提供了数量分析基础.

一种基于测距的无线传感器网络智能定位算法

提出了一种基于测距的无线传感器网的智能定位算法。建立了RSSI的理论计算模型;通过引入加权因子改进了DV-Distance定位算法(IDV-Distance)。实验结果表明:改进后的定位算法提高了定位精度,改善了系统的稳定性。

一种基于多维服务质量的局部最优服务选择模型

全局最优和局部最优是服务选择的两种策略.现有的全局最优服务选择算法提供端对端约束下最优单解而非可接受的多解,既无法充分体现用户偏好和服务个性,也不利于激励服务提供者优化服务质量.首先,在引入序数效用函数作为局部服务排序的数值尺度的基础上,提出一种基于多维服务质量的局部最优服务选择模型MLOMSS(Multi-QoS based Local Opti mal Model of Service Selection),为自动选取优质服务提供重要依据.然后,构造客观赋权模式、主观赋权模式和主客观赋权模式来确定各服务质量属性的权重,既体现用户偏好和服务质量的客观性,又有助于快速生成聚合服务链.最后,通过语义Web服务集成平台SEWSIP(Semantic Enable Web Serv-ice Integration Platform)证明MLOMSS模型的有效性和灵活性.

相似样本选择方法在SVM发酵建模中的应用

针对基于动态时间规整(DTW)发酵过程局部建模时相似样本选择未考虑样本权重对建模的影响,基于加权欧氏距离,提出了一种样本相似度度量方法。首先在分析亲和度的基础上将亲和度引入到加权欧式距离中,然后将其转化成相似度度量函数并应用到大肠杆菌发酵过程相似样本选择中,最后结合局部支持向量机建立发酵产物的在线预测模型。实验结果表明,与基于其他相似度度量函数的支持向量机模型相比较,该模型有着更高的预测精度、更好的泛化能力,而且预测时间也明显缩短。