基于随机森林模型的干旱绿洲区张掖盆地地下水水质评价

为合理准确评价地下水水质,建立了基于随机森林（RF）模型的地下水水质评价模型,并根据张掖盆地81个地下水采样点的pH值、Cl^-、SO₄^2-、NO₃^-、Na⁺、NH₄⁺含量及总硬度的监测数据,对研究区的地下水水质进行了综合评价。结果表明：盆地地下水水质主要为Ⅱ、Ⅲ、Ⅳ类水,其中甘州区地下水埋藏较深,水体不容易受到来自地面的污染,水质较好,大多数地方为Ⅱ类水;临泽县和高台县地下水埋藏较浅,水质较差,大多数地方为Ⅲ类水,尤其高台县的水位最浅,再加上地处河段下游,污染更为严重,部分地区达到Ⅳ类。根据指标的重要性度量发现影响研究区域地下水水质的主要因子是NO₃^-含量;其次是NH₄⁺、SO₄^2-、Na⁺、Cl^-含量及总硬度、pH值。为验证模型的有效性,将地下水水质评价结果与基于支持向量机（SVM）和人工神经网络（ANN）的地下水水质综合评价模型模拟结果进行对比,3个模型均能很好地评价研究区地下水水质,但RF模型的评价结果更为准确。     

基于FY-3的格陵兰岛冰盖表面冻融探测方法研究

格陵兰岛的冰盖对全球气候有着极为重要的意义,冰盖的冻融情况可直观展示北极地区的气候变化状况。利用我国FY-3气象卫星的微波成像仪(Microwave Radiation Imager,MWRI)数据,基于增加干湿雪差异性的交叉极化比率(Cross-Polarized Gradient Ratio,XPGR)算法,通过支持向量机(Support Vector Machine,SVM)的超平面进行格陵兰岛冰盖表面冻融探测,与已有的阈值方法相比,理论上精度较高。与微波辐射计(Special Sensor Microwave Image,SSM/I)(阈值为–0.025)的数据结果进行对比验证,结果表明:XPGR结合SVM的格陵兰岛冰盖表面冻融探测方法是可行的。     

Ablation Rate Estimates over the Greenland Ice Sheet from Microwave Radiometric Data

Scanning Multichannel Microwave Radiometer (SMMR) data are used to estimate the annual melt duration (number of days with melt) for elevation transects over the Greenland ice sheet during the period from 1979-1986. The annual melt duration is used to estimate the number of positive degree days (PDDs), which are used in a degree-day mass balance model to determine ablation rates and the equilibrium line altitude (ELA). The annual melt duration along two transects estimated with SMMR data compares favorably, particularly above the ELA, to melt duration calculated from surface temperature data for the same locations. The mass balance estimates and ELA locations along eight transects agree reasonably well with measurements reported in previous studies using surface temperature data. ELAs were within 10m of published values along two transects, and the root mean square error of SMMR-derived versus surface mass balance measurements was 43mm yr^?1. The estimated error in SMMR-derived ablation is between ±15% and ±50%, but could be reduced substantially by using daily microwave data available from the Special Sensor Microwave/Imager (SSM/I). This research shows the feasibility of using passive microwave data to estimate the ablation rate in order to determine ELA, which can be used to monitor the mass balance of the ice sheet.     

Ablation Rate Estimates over the Greenland Ice Sheet from Microwave Radiometric Data

Scanning Multichannel Microwave Radiometer (SMMR) data are used to estimate the annual melt duration (number of days with melt) for elevation transects over the Greenland ice sheet during the period from 1979‐1986. The annual melt duration is used to estimate the number of positive degree days (PDDs), which are used in a degree‐day mass balance model to determine ablation rates and the equilibrium line altitude (ELA). The annual melt duration along two transects estimated with SMMR data compares favorably, particularly above the ELA, to melt duration calculated from surface temperature data for the same locations. The mass balance estimates and ELA locations along eight transects agree reasonably well with measurements reported in previous studies using surface temperature data. ELAs were within 10m of published values along two transects, and the root mean square error of SMMR‐derived versus surface mass balance measurements was 43mm yr^?1. The estimated error in SMMR‐derived ablation is between ±15% and ±50%, but could be reduced substantially by using daily microwave data available from the Special Sensor Microwave/Imager (SSM/I). This research shows the feasibility of using passive microwave data to estimate the ablation rate in order to determine ELA, which can be used to monitor the mass balance of the ice sheet.     

Time series forecasting of river flow using an integrated approach of wavelet multi-resolution analysis and evolutionary data-driven models. A case study: Sebaou River (Algeria)

The complexity of hydrological processes and lack of data for modeling require the use of specific tools for non-linear natural phenomenon. In this paper, an effort has been made to develop a conjunction model – wavelet transformation, data-driven models, and genetic algorithm (GA) – for forecasting the daily flow of a river in northern Algeria using the time series of runoff. This catchment has a semi-arid climate and strong variability in runoff. The original time series was decomposed into multi-frequency time series by wavelet transform algorithm and used as inputs to artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models. Several factors must be optimized to determine the best model structures. Wavelet-based data-driven models using a GA are designed to optimize model structure. The performances of wavelet-based data-driven models (i.e. WANFIS and WANN) were superior to those of conventional models. WANFIS (RMSE = 12.15 m³/s, EC = 87.32%, R = .934) and WANN (RMSE = 15.73 m³/s, EC = 78.83%, R = .888) models improved the performances of ANFIS (RMSE = 23.13 m³/s, EC = 54.11%, R = .748) and ANN (RMSE = 22.43 m³/s, EC = 56.90%, R = .755) during the test period.     

Self-Learning Random Forests Model for Mapping Groundwater Yield in Data-Scarce Areas

Globally, groundwater plays a major role in supplying drinking water for urban and rural population and is used for irrigation to grow crops and in many industrial processes. A novel self-learning random forest (SLRF) model is developed and validated for groundwater yield zonation within the Yeondong Province in South Korea. This study was conducted with an inventory data initially divided randomly into 70% for training and 30% for testing and 13 groundwater-conditioning factors. SLRF was optimized using Bayesian optimization method. We also compared our method to other machine learning methods including support vector machine (SVM), artificial neural networks (ANN), decision trees (DT), and voting ensemble models. Model validation was accomplished using several methods, including a confusion matrix, receiver operating characteristics, cross-validation, and McNemar’s test. Our proposed self-learning method improves random forest (RF) generalization performance by about 23%, with SLRF success rates of 0.76 and prediction rates of 0.83. In addition, the optimized SLRF performed better [according to a threefold cross-validated AUC (area under curve) of 0.75] than that using randomly initialized parameters (0.57). SLRF outperformed all of the other models for the testing dataset (RF, SVM, ANN, DT, and Voted ANN-RF) when the overall accuracy, prediction rate, and cross-validated AUC metrics were considered. The SLRF also estimated the contribution of individual groundwater conditioning factors and showed that the three most influential factors were geology (1.00), profile curvature (0.97), and TWI (0.95). Overall, SLRF effectively modeled groundwater potential, even within data-scarce regions.

     

Application of Discriminant Analysis and Support Vector Machine in Mapping Gold Potential Areas for Further Drilling in the Sari-Gunay Gold Deposit,NW Iran

In this contribution, we used discriminant analysis (DA) and support vector machine (SVM) to model subsurface gold mineralization by using a combination of the surface soil geochemical anomalies and earlier bore data for further drilling at the Sari-Gunay gold deposit, NW Iran. Seventy percent of the data were used as the training data and the remaining 30 % were used as the testing data. Sum of the block grades, obtained by kriging, above the cutoff grade (0.5 g/t) was multiplied by the thickness of the blocks and used as productivity index (PI). Then, the PI variable was classified into three classes of background, medium, and high by using fractal method. Four classification functions of SVM and DA methods were calculated by the training soil geochemical data. Also, by using all the geochemical data and classification functions, the general extension of the gold mineralized zones was predicted. The mineral prediction models at the Sari-Gunay hill were used to locate high and moderate potential areas for further infill systematic and reconnaissance drilling, respectively. These models at Agh-Dagh hill and the area between Sari-Gunay and Agh-Dagh hills were used to define the moderate and high potential areas for further reconnaissance drilling. The results showed that the nu-SVM method with 73.8 % accuracy and c-SVM with 72.3 % accuracy worked better than DA methods.     

Goodnews Bay Platinum Resource Estimation Using Least Squares Support Vector Regression with Selection of Input Space Dimension and Hyperparameters

Resource estimation of a placer deposit is always a difficult and challenging job because of high variability in the deposit. The complexity of resource estimation increases when drill-hole data are sparse. Since sparsely sampled placer deposits produce high-nugget variograms, a traditional geostatistical technique like ordinary kriging sometimes fails to produce satisfactory results. In this article, a machine learning algorithm—the support vector machine (SVM)—is applied to the estimation of a platinum placer deposit. A combination of different neighborhood samples is selected for the input space of the SVM model. The trade-off parameter of the SVM and the bandwidth of the kernel function are selected by genetic algorithm learning, and the algorithm is tested on a testing data set. Results show that if eight neighborhood samples and their distances and angles from the estimated point are considered as the input space for the SVM model, the developed model performs better than other configurations. The proposed input space-configured SVM model is compared with ordinary kriging and the traditional SVM model (location as input) for resource estimation. Comparative results reveal that the proposed input space-configured SVM model outperforms the other two models.     

基于支持向量机和Getis因子的高分辨率遥感图像分类

采用支持向量机对具有RGB 3个波段、分辨率为0.32 m的航空摄影图像进行实验,首次根据表示空间聚集程度的局部Getis因子完成分类。结果表明:1)当应用基于线性、多项式、径向基和Sigmoid 4种常用核函数的SVM进行分类时,基于径向基的SVM分类精度最高,总体精度超过91%。2)从原始图像计算出局部Getis因子,该指标可用于图像分类,且分类精度与局部Getis因子的步长有关;在步长小于变异函数变程的条件下,应用径向基SVM的总体分类精度达95.66%,高于直接使用原始图像RGB波段光谱信息的分类精度,因此局部Getis因子在高空间分辨率遥感图像分类中具有应用和研究价值。     

基于遥感和机器学习的内陆水体水深反演技术

文章主要根据机器学习算法（随机森林算法和极端梯度提升算法）和遥感水深反演的原理,利用Sentinel_2多光谱卫星数据和无人船实测水深数据,对内陆水体——梅州水库建立了随机森林（RF）、极端梯度提升（XGBoost）和支持向量机（SVM）水深反演模型,并对反演结果进行对比分析。结果表明：1）RF的训练精度为97%,测试精度为0.80;XGBoost模型的训练精度为97%,测试精度为0.79;SVM的训练精度为90%,测试精度为0.78。说明了在水深预测方面RF模型和XGBoost模型比SVM模型表现更好,对各个区段的水深值较为敏感。2）根据运行时间考察各个模型的效率,其中RF模型从读取数据至输出结果耗时3.92 s;XGBoost模型4.26 s;SVM模型6.66 s。因此,在反演精度和效率上RF模型优于XGBoost模型优于SVM模型,且RF模型的预测结果图细节更加丰富,轮廓更加分明;XGBoost模型次之,但总体效果也较好;SVM模型表现最差。由此可知,机器学习水深反演模型获得的水深结果精度明显提高,解决了传统水深反演模型精度不高的问题。     

Artificial neural networks and geostatistical models for housing valuations in urban residential areas

Property valuation studies often use classical statistics techniques. Among these techniques, the Artificial Neural Networks are the most applied, overcoming the inflexibility and the linearity of the hedonic models. Other researchers have used Geostatistics techniques, specifically the Kriging Method, for interpreting spatial-temporal variability and to predict housing unit prices. The innovation of this study is to highlight how the Kriging Method can help to better understand the urban environment, improving the results obtained by classical statistics. This study presents two different methods that share the general objective of extracting information regarding a city’s housing from datasets. The procedures applied are Ordinary Kriging (Geostatistics) and Multi-Layer Perceptron algorithm (Artificial Neural Networks). These methods were used to predict housing unit prices in the municipality of Pozuelo de Alarcon (Madrid). The implementation of both methods provides us with the urban characteristics of the study area and the most significant variables related to price. The main conclusion is that the Ordinary Kriging models and the Neural Networks models, applied to predicting housing unit prices are necessary methodologies to improve the information obtained in classical statistical techniques.

Abbreviations: ANN: Artificial Neural Networks; OK: ordinary Kriging; MLP: multi-layer perceptron     

Simulation of urban expansion via integrating artificial neural network with Markov chain – cellular automata

Accurate simulations and predictions of urban expansion are critical to manage urbanization and explicitly address the spatiotemporal trends and distributions of urban expansion. Cellular Automata integrated Markov Chain (CA-MC) is one of the most frequently used models for this purpose. However, the urban suitability index (USI) map produced from the conventional CA-MC is either affected by human bias or cannot accurately reflect the possible nonlinear relations between driving factors and urban expansion. To overcome these limitations, a machine learning model (Artificial Neural Network, ANN) was integrated with CA-MC instead of the commonly used Analytical Hierarchy Process (AHP) and Logistic Regression (LR) CA-MC models. The ANN was optimized to create the USI map and then integrated with CA-MC to spatially allocate urban expansion cells. The validated results of kappa and fuzzy kappa simulation indicate that ANN-CA-MC outperformed other variously coupled CA-MC modelling approaches. Based on the ANN-CA-MC model, the urban area in South Auckland is predicted to expand to 1340.55 ha in 2026 at the expense of non-urban areas, mostly grassland and open-bare land. Most of the future expansion will take place within the planned new urban growth zone.     

Global retail capital and the city: towards an intensification of gentrification

The emerging literature on retail gentrification has not paid much attention to the link between recent reconfigurations of retail capital (concentration, internationalization, and financialization) and the contemporary wave of “generalized gentrification”. In this paper, I argue that analyzing the strategies of stakeholders involved in sectors other than housing (in this case, the retail sector) should allow us to identify different forms of gentrification intensification. I investigate the case of the Marais – that is, one of the first Parisian neighborhoods having undergone gentrification – by mapping the frontier of retail gentrification over the long term (1965–2011) and at the scale of an entire neighborhood (more than 130 ha comprising over 2,000 commercial units). The key drivers of the process (commercial real estate, new brand development strategies, changing commercial environment, the role of public policies) and its social stakes (displacement/replacement of former stores) are then discussed.     

Role of hydrological regime and floodplain sediments in channel instability of the Bhagirathi River,Ganga-Brahmaputra Delta,India

Abstract

This research deals with the surface dynamics and key factors – hydrological regime, sediment load, and erodibility of floodplain facies – of frequent channel shifting, intensive meandering, and lateral instability of the Bhagirathi River in the western part of the Ganga-Brahmaputra Delta (GBD). At present, the floodplain of the Bhagirathi is categorized as a medium energy (specific stream power of 10–300 W m^?2), non-cohesive floodplain, which exhibits a mixed-load and a meandering channel, an entrenchment ratio >2.2, width–depth ratio >12, sinuosity >1.4, and channel slope <0.02. In the study area, since 1975, four meander cutoffs have been shaped at an average rate of one in every 9–10 years. In the active meander belt and sand-silt dominated floodplains of GBD, frequent shifting of the channel and meander migration escalate severe bank erosion (e.g. 2.5 × 10⁶ m³ of land lost between 1999 and 2004) throughout the year. Remote sensing based spatio-temporal analysis and stratigraphic analysis reveal that the impact of the Farakka barrage, completed in 1975, is not the sole factor of downstream channel oscillation; rather, hydrogeomorphic instability induced by the Ajay–Mayurakshi fluvial system and the erodibility of floodplain sediments control the channel dynamics of the study area.     

Derivative‐Neural Spectroscopy for Hyperspectral Bathymetric Inversion*

Bathymetry is an important variable in scientific and operational applications. The research objectives in this study were to estimate bathymetry based on derivative reflectance spectra used as input to a multilayer perceptron artificial neural network (ANN) and to evaluate the efficacy of field and simulated training/testing data. ANNs were used to invert reflectance field data acquired in optically shallow coastal waters. Results indicate that for the simulation‐based models, nonderivative spectra yielded more accurate bathymetry retrievals than the derivative spectra used as ANN input. However, for the empirical field‐based models, derivative spectra were superior to nonderivative spectra as ANN input. This study identifies circumstances under which derivative spectra are useful in bathymetry estimation, and thus increases the likelihood of obtaining accurate inversions.     

Improving sea ice type discrimination by the simultaneous use of SSM/I and scatterometer data

The multi-year sea ice (MY) concentration as determined with the NASA Team algorithm (NTA) shows an increase during winter. This unrealistic feature can be reduced using combined active and passive remote sensing data, leading to a more realistic estimation of MY area. Our joint analysis of SSM/I, QuikSCATterometer (QSCAT) and meteorological data reveals events (i.e. intervals in space and time) where increased surface roughness and volume scattering, after a melt-refreezing episode, alters the passive microwave signature of the undisturbed sea ice surface. In these events, the calculation of MY and FY areas employing the NTA leads to false estimations of their amounts. It is shown that when such events occur, QSCAT backscatter values increase by more than 3 dB. This backscatter variation can be easily detected and the FY and MY area determination of the NTA can be corrected accordingly within defined event-regions. Using this method, called Simultaneous NTA, we found that in May 2000 12% of the area detected by the NTA as MY has to be corrected to FY. As a consequence, a detailed reanalysis of the 20-year passive microwave data set is suggested to more precisely compute the MY area.     

Spatio-temporal modeling of PM2.5 concentrations with missing data problem: a case study in Beijing,China

ABSTRACT

One of the major challenges in conducting epidemiological studies of air pollution and health is the difficulty of estimating the degree of exposure accurately. Fine particulate matter (PM_2.5) concentrations vary in space and time, which are difficult to estimate in rural, suburban and smaller urban areas due to the sparsity of the ground monitoring network. Satellite retrieved aerosol optical depth (AOD) has been increasingly used as a proxy of ground PM_2.5 observations, although it suffers from non-trivial missing data problems. To address these issues, we developed a multi-stage statistical model in which daily PM_2.5 concentrations can be obtained with complete spatial coverage. The model consists of three stages – an inverse probability weighting scheme to correct non-random missing patterns of AOD values, a spatio-temporal linear mixed effect model to account for the spatially and temporally varying PM_2.5-AOD relationships, and a gap-filling model based on the integrated nested Laplace approximation-stochastic partial differential equations (INLA-SPDE). Good model performance was achieved from out-of-sample validation as shown in R² of 0.93 and root mean square error of 9.64 μg/m³. The results indicated that the multi-stage PM_2.5 prediction model proposed in the present study yielded highly accurate predictions, while gaining computational efficiency from the INLA-SPDE.     

厚度对月壤微波辐射亮温的影响

基于嫦娥系列卫星微波辐射计数据的月壤厚度反演是中国月球科学研究的重要目标之一。基于辐射传输方程,数值模拟了不同频率、（FeO+TiO₂）含量和表面温度条件下厚度对月壤微波辐射亮温的影响;基于嫦娥二号卫星微波辐射计（Chang’E Lunar Microwave Sounder,CELMS）数据,结合Apollo 计划获取的月壤厚度资料及其他月壤厚度资料,系统分析了厚度对CELMS观测数据的影响。结果表明：频率、（FeO+TiO₂）含量、表面温度对亮温的影响远大于厚度对亮温的影响,是基于CELMS数据进行月壤厚度反演的重要影响因素;低频、低（FeO+TiO₂）含量、低温条件下,厚度对CELMS数据的影响最大;利用3 GHz、凌晨时刻的CELMS数据进行月陆地区月壤厚度反演可行。研究结果对基于嫦娥系列卫星CELMS数据的月壤厚度反演具有重要参考意义。     

Comparison of dew point temperature estimation methods in Southwestern Georgia

Recent upward trends in acres irrigated have been linked to increasing near-surface moisture. Unfortunately, stations with dew point data for monitoring near-surface moisture are sparse. Thus, models that estimate dew points from more readily observed data sources are useful. Daily average dew temperatures were estimated and evaluated at 14 stations in Southwest Georgia using linear regression models and artificial neural networks (ANN). Estimation methods were drawn from simple and readily available meteorological observations, therefore only temperature and precipitation were considered as input variables. In total, three linear regression models and 27 ANN were analyzed. The two methods were evaluated using root mean square error (RMSE), mean absolute error (MAE), and other model evaluation techniques to assess the skill of the estimation methods. Both methods produced adequate estimates of daily averaged dew point temperatures, with the ANN displaying the best overall skill. The optimal performance of both models was during the warm season. Both methods had higher error associated with colder dew points, potentially due to the lack of observed values at those ranges. On average, the ANN reduced RMSE by 6.86% and MAE by 8.30% when compared to the best performing linear regression model.     

1961—1987年与1988—2014年辽宁省气候区划界线变化及可能气候成因

基于同一区划方法、指标体系,使用1961—2014年辽宁省52站气象观测资料,分析辽宁省气温、气候区划指标、范围及界线的变动特征。结果表明：辽宁省年均气温在1988年发生一次突变,突变后气温开始显著上升;≥10 ℃积温日数比较显著地响应气温突变,而干燥指数、7月平均气温变化不显著。在空间分布上区划指标值均存在不同程度的变化。① 全省≥10 ℃积温日数均出现增加,但在中西部地区显著增加;② 在盘锦-抚顺一线以北（南）,气候总体呈不显著变湿（干）趋势;③ 7月平均气温呈缓慢上升趋势。区划范围及界线位置出现更加显著地变化：① 暖温带范围主要向北向东扩展,中温带向东收缩;② 半湿润区范围主要向北向西扩展,半干旱区向西北方向收缩,湿润区范围基本不变;③ Tb范围显著向北向东扩展,Ta范围向北向东收缩。在此基础上分析了气候格局变化的可能气候成因,发现突变后≥10 ℃积温日数期间500 hPa高度场增加与4月和10月东亚冬季风减弱,4—10月东北冷涡持续天数增加和7月500 hPa高度场增加,可能分别是温度带,Tb区、Ta区和半湿润区、半干旱区变化的原因。