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1.
基于最优加权组合模型及高斯－牛顿法的滑坡变形预测研究 总被引：1，自引：0，他引：1
在对最优加权组合理论和高斯－牛顿法优化非线性模型参数的方法研究的基础上,依托于洒勒山滑坡的实际变形监测资料,建立了该滑坡变形预测的3个非线性预测模型:指数模型、Verhulst模型和灰色GM（1,1）模型;利用最优加权组合理论建立了洒勒山滑坡的最优加权组合预测模型,并运用高斯－牛顿法对各单一模型和组合模型的参数进行了优化。通过对比分析得出:组合模型的预测精度高于任何单一模型的预测精度;参数优化后各单一模型的预测精度都有不同程度的提高;参数优化后的组合模型预测精度是最高的。因此,综合运用最优组合理论和高斯－牛顿法处理滑坡预测预报模型,是提高滑坡预测预报精度的行之有效的方法。 相似文献
2.
基于Budyko水热耦合平衡假设,推导了年径流变化的计算公式,分析了长江流域多年平均潜在蒸发量、降水量、干旱指数和敏感性参数的空间变化规律。选用BCC-CSM1-1全球气候模式和RCP4.5排放情景,把未来气候要素预估值与LS-SVM统计降尺度方法相耦合,预测长江流域未来的气温、降水和径流变化情况。采用乌江和汉江流域的长期径流观测资料,分析验证了基于Budyko公式计算年径流变化的可靠性。结果表明:降水量变化是影响径流量变化的主导因素;长江各子流域未来径流相对变化增减不一,最大变幅10%左右;在未来2020s(2010—2039年)、2050s(2040—2069年)和2080s(2070—2099年)3个时期内,长江南北两岸流域的径流将出现"南减北增"现象,北岸径流变化增幅逐渐升高,南岸径流变化减幅逐渐降低。 相似文献
3.
The purpose of this paper is to provide a proper, practical and convenient drilling rate index (DRI) prediction model based on rock material properties. In order to obtain this purpose, 47 DRI tests were used. In addition, the relevant strength properties i.e. uniaxial compressive strength and Brazilian tensile strength were also used and selected as input parameters to predict DRI. Examined simple regression analysis showed that the relationships between the DRI and predictors are statistically meaningful but not good enough for DRI estimation in practice. Moreover, multiple regression, artificial neural network (ANN) and hybrid genetic algorithm (GA)-ANN models were constructed to estimate DRI. Several performance indices i.e. coefficient of determination (R2), root mean square error and variance account for were used for evaluation of performance prediction the proposed methods. Based on these results and the use of simple ranking procedure, the best models were chosen. It was found that the hybrid GA-ANN technique can performed better in predicting DRI compared to other developed models. This is because of the fact that the proposed hybrid model can update the biases and weights of the network connection to train by ANN. 相似文献
4.
基于ANN与GIS技术的区域岩溶塌陷稳定性预测———以桂林西城区为例 总被引：6，自引：0，他引：6
岩溶地面塌陷是岩溶区常见的一种地质灾害, 塌陷区域预测是进行国土规划、资源开发与灾害防治的必要工作.由于岩溶塌陷的影响因素众多且相互作用, 发展过程复杂, 加之各评价因子的数值获取困难, 致使长期以来塌陷区域定量预测成为一个难以解决的课题.现行的区域预测模型不能描述塌陷形成模式的非线性特征, 也难以克服评价因子权重确定过程中人为经验因素的影响.神经网络技术的自学习、自适应与高度非线性映射特点显示了其在塌陷区域预测领域中应用的前景.根据研究区内地面塌陷空间聚集分布的特征, 提出了不同因子组合条件下塌陷发生可能性的定量化方法, 结合选定的评价因子类别确定了神经网络预测模型的结构, 利用312个塌陷点样本中的292个进行网络训练, 余下的20个样本的校验结果表明该模型具有较高的可信度.运用GIS技术将研究区进行评价单元划分, 并获取各评价因子的取值, 输入到训练好的网络中进行预测.将各单元的输出值进行归并处理后得到研究区岩溶塌陷的稳定级分区图. 相似文献
5.
Knowledge of pore-water pressure(PWP)variation is fundamental for slope stability.A precise prediction of PWP is difficult due to complex physical mechanisms and in situ natural variability.To explore the applicability and advantages of recurrent neural networks(RNNs)on PWP prediction,three variants of RNNs,i.e.,standard RNN,long short-term memory(LSTM)and gated recurrent unit(GRU)are adopted and compared with a traditional static artificial neural network(ANN),i.e.,multi-layer perceptron(MLP).Measurements of rainfall and PWP of representative piezometers from a fully instrumented natural slope in Hong Kong are used to establish the prediction models.The coefficient of determination(R^2)and root mean square error(RMSE)are used for model evaluations.The influence of input time series length on the model performance is investigated.The results reveal that MLP can provide acceptable performance but is not robust.The uncertainty bounds of RMSE of the MLP model range from 0.24 kPa to 1.12 k Pa for the selected two piezometers.The standard RNN can perform better but the robustness is slightly affected when there are significant time lags between PWP changes and rainfall.The GRU and LSTM models can provide more precise and robust predictions than the standard RNN.The effects of the hidden layer structure and the dropout technique are investigated.The single-layer GRU is accurate enough for PWP prediction,whereas a double-layer GRU brings extra time cost with little accuracy improvement.The dropout technique is essential to overfitting prevention and improvement of accuracy. 相似文献
6.
为了分析边坡的稳定性,利用协调粒子群算法和BP网络建立了边坡稳定性CPSO-BP预测模型。BP网络能够很好地描述边坡稳定性与其影响因素之间复杂的非线性关系,将内摩擦角、边坡角、岩石重度、边坡高度、黏聚力、孔隙压力比6个主要影响因素作为网络的输入,将边坡稳定性系数作为网络的输出。为避免BP网络陷入局部最优,利用协调粒子群算法的全局优化能力确定BP网络的连接权值和阀值,使BP网络的优势得到分发挥,达到提高模型预测精度目的。实例表明CPSO-BP模型有更好地预测精度以及将其应用于边坡稳定性预测是可行的。 相似文献
7.
Assessment of soil erosion in a tropical mountain river basin of the southern Western Ghats,India using RUSLE and GIS 总被引：1，自引：0，他引：1
Revised Universal Soil Loss Equation(RUSLE) model coupled with transport limited sediment delivery(TLSD) function was used to predict the longtime average annual soil loss, and to identify the critical erosion-/deposition-prone areas in a tropical mountain river basin, viz., Muthirapuzha River Basin(MRB; area=271.75 km~2), in the southern Western Ghats, India. Mean gross soil erosion in MRB is 14.36 t ha~(-1) yr~(-1), whereas mean net soil erosion(i.e., gross erosion-deposition) is only 3.60 t ha~(-1) yr~(-1)(i.e., roughly 25% of the gross erosion). Majority of the basin area(~86%) experiences only slight erosion(5 t ha~(-1) yr~(-1)), and nearly 3% of the area functions as depositional environment for the eroded sediments(e.g., the terraces of stream reaches, the gentle plains as well as the foot slopes of the plateau scarps and the terrain with concordant summits). Although mean gross soil erosion rates in the natural vegetation belts are relatively higher, compared to agriculture, settlement/built-up areas and tea plantation, the sediment transport efficiency in agricultural areas and tea plantation is significantly high,reflecting the role of human activities on accelerated soil erosion. In MRB, on a mean basis, 0.42 t of soil organic carbon(SOC) content is being eroded per hectare annually, and SOC loss from the 4th order subbasins shows considerable differences, mainly due to the spatial variability in the gross soil erosion rates among the sub-basins. The quantitative results, on soil erosion and deposition, modelled using RUSLE and TLSD, are expected to be beneficial while formulating comprehensive land management strategies for reducing the extent of soil degradation in tropical mountain river basins. 相似文献
8.
Most studies using GRACE (Gravity Recovery and Climate Experiment) data for examining water storage anomalies have rich hydrogeological databases. Here, GRACE data are analyzed for southern Mali, Africa, a region with sparse hydrogeological data. GRACE data (2002?C2008) did not overlap with observed groundwater-level data (1982?C2002). Terrestrial water storage from GRACE was corrected for soil moisture using the Global Land Data Assimilation System (GLDAS) model to obtain monthly groundwater storage anomalies and annual net recharge. Historical storage anomalies and net recharge were determined using the water-table fluctuation method for available observation wells. Average annual net recharge averaged 149.1?mm (or 16.4% of annual rainfall) and 149.7?mm (14.8%) from historical water level and GRACE data, respectively. Monthly storage anomaly lows and peaks were observed in May and September, respectively, but have a shift in peak to November using the corrected GRACE data, suggesting that the GLDAS model may poorly predict the timing of soil-water storage in this region. Notwithstanding problems with the GLDAS model, the soil moisture-corrected GRACE data accurately predict the relative timing and magnitude of groundwater-storage changes, suggesting that GRACE data are valuable for identifying long-term regional changes in groundwater storage in areas with sparse hydrogeological data. 相似文献
9.
10.
基于粒子群优化神经网络算法的深基坑变形预测方法 总被引：1，自引：0，他引：1
深基坑变形预测是进行施工参数调整和确保深基坑施工安全的重要手段,而如何对其变形进行有效、准确的预测是一个有待解决的技术难题。采用粒子群优化算法对神经网络模型的初始权值和阈值进行优化,并将已有的变形监测数据作为神经网络的输入参数,建立了基于粒子群优化神经网络算法的深基坑变形预测方法。将形成的方法应用于长春市火车站北广场深基坑开挖监测工程中。结果表明：8号水平位移测点预测结果的均方根误差为3.78%,平均百分比误差为5.48%;9号地面沉降点预测结果的均方根误差为5.62%,平均百分比误差为3.23%。经验证,本文方法预测深基坑开挖过程中的变形具有较高的可信度。 相似文献
11.
基于证据权法的赣南稀土矿山地质环境评价 总被引：1，自引：0，他引：1
为了解决矿山地质环境评价研究中指标权值计算和评价分级具有较大主观性的问题,本文发展了一种基于证据权法的矿山地质环境评价模式,利用GIS的空间分析功能,提取矿产开发活动、坡度、坡向、高程、构造、植被覆盖度6类地质环境评价指标,运用证据权法计算指标权值。结合条件独立性检验结果,优选评价指标并计算后验概率。采用后验概率面积频率法对赣南稀土矿山地质环境进行综合评价分级。结果表明,研究区内地质环境差区域主要分布在其东南部和西部;矿产开发活动是影响研究区内地质环境质量最主要的因素。采用效率曲线法对模型验证,正确率和预测率分别为90.1%和89.5%。证据权法能够有效避免主观因素干扰,评价结果客观,具有可重现性,适用于矿山地质环境质量评价。 相似文献
12.
Phytoplankton variability is a primary driver of chemical and biological dynamics in the coastal zone because it directly affects water quality, biogeochemical cycling of reactive elements, and food supply to consumer organisms. Much has been learned about patterns of phytoplankton variability within individual ecosystems, but patterns have not been compared across the diversity of ecosystem types where marine waters are influenced by connectivity to land. We extracted patterns from chlorophyll-a series measured at 84 estuarine–coastal sites, using a model that decomposes time series into an annual effect, mean seasonal pattern, and residual “events.” Comparisons across sites revealed a large range of variability patterns, with some dominated by a recurrent seasonal pattern, others dominated by annual (i.e., year-to-year) variability as trends or regime shifts and others dominated by the residual component, which includes exceptional bloom events such as red tides. Why is the partitioning of phytoplankton variability at these three scales so diverse? We propose a hypothesis to guide next steps of comparative analysis: large year-to-year variability is a response to disturbance from human activities or shifts in the climate system; strong seasonal patterns develop where the governing processes are linked to the annual climate cycle; and large event-scale variability occurs at sites highly enriched with nutrients. Patterns of phytoplankton variability are therefore shaped by the site-specific relative importance of disturbance, annual climatology, and nutrient enrichment. 相似文献
13.
三峡库区某些库岸滑坡在强降雨、库水位涨落等诱发因素影响下,其位移时间序列表现出阶跃式变化特征且可能存在混沌特性.但目前常用于滑坡位移预测的混沌模型,均建立在单变量混沌理论的基础之上.且已有的考虑了诱发因素的常规多变量模型,大都采用经验性的方法来选取输入变量;常规多变量模型对滑坡位移序列的非线性特征,及其与诱发因素间的动态响应关系缺乏数学理论上的深入分析.因此,提出一种基于指数平滑法、多变量混沌模型和极限学习机（extreme learing machine,ELM）的滑坡位移组合预测模型.指数平滑多变量混沌ELM模型首先对滑坡累积位移序列的混沌特性进行识别;然后用指数平滑法对累积位移进行预测,得到趋势项位移,并用累积位移减去趋势项位移得到剩余的波动项位移;之后对波动项位移及降雨量、库水位变化量这3个因子进行多变量相空间重构,并用ELM模型对多变量重构后的波动项位移进行预测;最后将预测得到的趋势项和波动项位移值相加,得到最终的累积位移预测值.以三峡库区白水河滑坡ZG93监测点的累积位移作为实例进行分析,并将模型与指数平滑多变量混沌粒子群-支持向量机（PSO-SVM）模型、指数平滑单变量混沌ELM模型作对比.结果表明滑坡位移序列存在混沌特性,模型能有效预测滑坡位移,其预测效果优于对比模型.且本文模型从混沌理论的角度将波动项位移与降雨量、库水位变化量的动态响应关系进行综合分析,更能反映滑坡位移系统演化的物理本质. 相似文献
14.
证据权方法是目前最常用的信息综合方法之一,广泛应用于矿产资源定量预测与评价.然而,它要求变量间相互独立,地质上很难满足这一条件.如何削弱条件不独立对证据权预测结果的影响,已成为当前数学地球科学研究的热点.解决该问题的途径之一是对传统证据权模型进行校正,比如采取加权的方法对原证据权模型计算的证据权重进行修正,以便消除非条件独立性的影响.对近期提出的多种加权证据权模型进行了系统的对比研究,基于同样的应用实例和实验方案,对不同方法的应用效果进行了比较,结果表明,各种加权证据权模型均可不同程度地削弱证据图层条件不独立性的影响,其中,基于逻辑回归的加权证据权模型优于其他加权方法. 相似文献
15.
《地学前缘(英文版)》2017,8(5):941-949
There are serious concerns of rise in temperatures over snowy and glacierized Himalayan region that may eventually affect future river flows of Indus river system. It is therefore necessary to predict snow and glacier melt runoff to manage future water resource of Upper Indus Basin(UIB). The snowmelt runoff model(SRM) coupled with MODIS remote sensing data was employed in this study to predict daily discharges of Gilgit River in the Karakoram Range. The SRM was calibrated successfully and then simulation was made over four years i.e. 2007, 2008, 2009 and 2010 achieving coefficient of model efficiency of 0.96, 0.86, 0.9 and 0.94 respectively. The scenarios of precipitation and mean temperature developed from regional climate model PRECIS were used in SRM model to predict future flows of Gilgit River. The increase of 3 C in mean annual temperature by the end of 21 th century may result in increase of 35-40% in Gilgit River flows. The expected increase in the surface runoff from the snow and glacier melt demands better water conservation and management for irrigation and hydel-power generation in the Indus basin in future. 相似文献
16.
Hadi Fattahi 《Environmental Earth Sciences》2017,76(5):224
Circular failure is generally observed in the slope of soil, highly jointed rock mass, mine dump and weak rock. Accurate estimation of the safety factor (SF) of slopes and their performance is not an easy task. In this research, based on rock engineering systems (RES), a new approach for the estimation of the SF is presented. The introduced model involves six effective parameters on SF [unit weight (γ), pore pressure ratio (r u), height (H), angle of internal friction (φ), cohesion (C) and slope angle (\(\beta\))], while retaining simplicity as well. In the case of SF prediction, all the datasets were divided randomly to training and testing datasets for proposing the RES model. For comparison purposes, nonlinear multiple regression models were also employed for estimating SF. The performances of the proposed predictive models were examined according to two performance indices, i.e., coefficient of determination (R 2) and mean square error. The obtained results of this study indicated that the RES is a reliable method to predict SF with a higher degree of accuracy in comparison with nonlinear multiple regression models. 相似文献
17.
Chengyu Xie Hoang Nguyen Xuan-Nam Bui Yosoon Choi Jian Zhou Thao Nguyen-Trang 《地学前缘(英文版)》2021,12(3):101108
Blasting is well-known as an effective method for fragmenting or moving rock in open-pit mines. To evaluate the quality of blasting, the size of rock distribution is used as a critical criterion in blasting operations. A high percentage of oversized rocks generated by blasting operations can lead to economic and environmental damage. Therefore, this study proposed four novel intelligent models to predict the size of rock distribution in mine blasting in order to optimize blasting parameters, as well as the efficiency of blasting operation in open mines. Accordingly, a nature-inspired algorithm (i.e., firefly algorithm – FFA) and different machine learning algorithms (i.e., gradient boosting machine (GBM), support vector machine (SVM), Gaussian process (GP), and artificial neural network (ANN)) were combined for this aim, abbreviated as FFA-GBM, FFA-SVM, FFA-GP, and FFA-ANN, respectively. Subsequently, predicted results from the abovementioned models were compared with each other using three statistical indicators (e.g., mean absolute error, root-mean-squared error, and correlation coefficient) and color intensity method. For developing and simulating the size of rock in blasting operations, 136 blasting events with their images were collected and analyzed by the Split-Desktop software. In which, 111 events were randomly selected for the development and optimization of the models. Subsequently, the remaining 25 blasting events were applied to confirm the accuracy of the proposed models. Herein, blast design parameters were regarded as input variables to predict the size of rock in blasting operations. Finally, the obtained results revealed that the FFA is a robust optimization algorithm for estimating rock fragmentation in bench blasting. Among the models developed in this study, FFA-GBM provided the highest accuracy in predicting the size of fragmented rocks. The other techniques (i.e., FFA-SVM, FFA-GP, and FFA-ANN) yielded lower computational stability and efficiency. Hence, the FFA-GBM model can be used as a powerful and precise soft computing tool that can be applied to practical engineering cases aiming to improve the quality of blasting and rock fragmentation. 相似文献
18.
Prediction of ground vibration due to quarry blasting based on gene expression programming: a new model for peak particle velocity prediction 总被引：1，自引：1，他引：0
R Shirani Faradonbeh D Jahed Armaghani M. Z. Abd Majid M. MD Tahir B. Ramesh Murlidhar M. Monjezi H. M. Wong 《International Journal of Environmental Science and Technology》2016,13(6):1453-1464
Blasting is a widely used technique for rock fragmentation in opencast mines and tunneling projects. Ground vibration is one of the most environmental effects produced by blasting operation. Therefore, the proper prediction of blast-induced ground vibrations is essential to identify safety area of blasting. This paper presents a predictive model based on gene expression programming (GEP) for estimating ground vibration produced by blasting operations conducted in a granite quarry, Malaysia. To achieve this aim, a total number of 102 blasting operations were investigated and relevant blasting parameters were measured. Furthermore, the most influential parameters on ground vibration, i.e., burden-to-spacing ratio, hole depth, stemming, powder factor, maximum charge per delay, and the distance from the blast face were considered and utilized to construct the GEP model. In order to show the capability of GEP model in estimating ground vibration, nonlinear multiple regression (NLMR) technique was also performed using the same datasets. The results demonstrated that the proposed model is able to predict blast-induced ground vibration more accurately than other developed technique. Coefficient of determination values of 0.914 and 0.874 for training and testing datasets of GEP model, respectively show superiority of this model in predicting ground vibration, while these values were obtained as 0.829 and 0.790 for NLMR model. 相似文献
19.
准确预测露天矿边坡变形是有效实现边坡临灾预警的重要保证,针对传统边坡变形预测方法无法表征和综合分析边坡变形受多种因素的影响,提出一种露天矿边坡变形的人工蜂群(ABC)算法优化广义回归网络(GRNN)组合预测模型(ABC-GRNN)。在此预测模型中,综合考虑了影响露天矿边坡变形的5个因素：开采扰动、降雨量、降雨持续时间、温度以及湿度。以山西中煤平朔安家岭露天矿为例,通过遗传算法改进BP神经网络(GA-BPNN)、支持向量机(SVM)等人工智能算法与实测变形数据进行预测效果对比分析。结果表明：ABC算法能够快速帮助GRNN寻优获取合适的传递参数,并对变形进行有效的预测。ABC-GRNN组合预测模型,将预测结果的平均绝对误差292.9 mm、平均绝对百分比误差0.691 3%及均方根误差338.9 mm分别降低到25 mm、0.043 3%和29.5 mm,说明该模型具有更高的预测精度;ABC-GRNN模型比其他模型收敛速度快,只经过7步的迭代,即可得到最小的均方误差。与其他预测模型相比较,本文模型的预测精度更高、泛化能力更强、收敛速度更快,有较高的实用价值。 相似文献
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为了建立高精度的边坡位移预测模型,文章采用基于粒子群优化(PSO)的双稀疏相关向量机(DSRVM)建立边坡稳定性和影响因素之间的非线性关系。双稀疏相关向量机是在变分和相关向量机(RVM)框架下提出的一种多核组合优化的方法,相比于RVM和其他多核学习方法,DSRVM不仅有更少的训练时间,并且能够得到更高的预测精度。由于DSRVM的核参数对预测效果的影响较大,文章采用粒子群算法实现多个核参数的优化选取并应用于边坡位移预测。最后将本文提出的基于粒子群优化的双稀疏相关向量机(PSO-DSRVM)预测结果与极限学习机(ELM)和小波神经网络(WNN)预测结果进行对比,通过均方根误差(RMSE)、复相关系数(R2)和平均相对预测误差(ARPE)进行评价,验证了PSO-DSRVM模型在边坡变形预测上的可行性。 相似文献