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1.
Strict control of the environmental impacts of blasting operations needs to be completely in line with the regulatory limits. In such operations, flyrock control is of high importance especially due to safety issues and the damages it may cause to infrastructures, properties as well as the people who live within and around the blasting site. Such control causes flyrock to be limited, hence significantly reducing the risk of damage. This paper serves two main objectives: risk assessment and prediction of flyrock. For these objectives, a fuzzy rock engineering system (FRES) framework was developed in this study. The proposed FRES was able to efficiently evaluate the parameters that affect flyrock, which facilitate decisions to be made under uncertainties. In this study, the risk level of flyrock was determined using 11 independent parameters, and the proposed FRES was capable of calculating the interactions among these parameters. According to the results, the overall risk of flyrock in the studied case (Ulu Tiram quarry, located in Malaysia) was medium to high. Hence, the use of controlled blasting method can be recommended in the site. In the next step, three optimization algorithms, namely genetic algorithm (GA), imperialist competitive algorithm (ICA) and particle swarm optimization (PSO), were used to predict flyrock, and it was found that the GA-based model was more accurate than the ICA- and PSO-based models. Accordingly, it is concluded that FRES is a very useful for both risk assessment and prediction of flyrock. 相似文献
2.
Flyrock is one of the most important environmental issues in mine blasting, which can affect equipment, people and could cause fatal accidents. Therefore, minimization of this environmental issue of blasting must be considered as the ultimate objective of many rock removal projects. This paper describes a new minimization procedure of flyrock using intelligent approaches, i.e., artificial neural network (ANN) and particle swarm optimization (PSO) algorithms. The most effective factors of flyrock were used as model inputs while the output of the system was set as flyrock distance. In the initial stage, an ANN model was constructed and proposed with high degree of accuracy. Then, two different strategies according to ideal and engineering condition designs were considered and implemented using PSO algorithm. The two main parameters of PSO algorithm for optimal design were obtained as 50 for number of particle and 1000 for number of iteration. Flyrock values were reduced in ideal condition to 34 m; while in engineering condition, this value was reduced to 109 m. In addition, an appropriate blasting pattern was proposed. It can be concluded that using the proposed techniques and patterns, flyrock risks in the studied mine can be significantly minimized and controlled. 相似文献
3.
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. 相似文献
4.
基于国内现行的森林火险气象指数和单因子火险贡献度模型,以及逻辑回归模型和随机森林模型,在林火预报中引入微波遥感土壤水分信息,使用MCD14DL火点数据集和地面气象观测资料对广东省不同时间尺度的林火发生概率进行预测。结果表明：逻辑回归模型和随机森林模型构建的林火预测模型显著优于现行的森林火险气象指数和单因子火险贡献度模型,预测精度提升约20%。其中,随机森林模型对林火频数的解释程度最高（两者相关系数为0.476）。此外,加入微波土壤水分信息后,相较原有的基于气象要素的林火预测模型,2种机器学习模型的预测精度均略有提升,体现了表层土壤水分信息在林火预报中的重要性。研究可为高效提取对地观测信息,以改进华南地区不同时间尺度的林火预报工作提供参考。 相似文献
5.
提高干旱预测精度能为流域干旱应对及风险防范提供可靠数据支撑,构建比选合适的干旱模型是当前研究的热点。研究以4个时间尺度（3、6、9、12月）标准化降水指数（SPI）为表征指标,利用小波神经网络（WNN）、支持向量回归（SVR）、随机森林（RF）三种机器学习算法分别构建了海河北系干旱预测模型,利用Kendall、K-S、MAE三种检验方法判定模型表现及其稳定性。研究表明：（1） WNN、SVR模型呈现结果在不同时间尺度SPI存在差异,WNN最适合12个月尺度SPI干旱预测;SVR最适合6个月尺度SPI干旱预测。（2） 对3、12个月尺度SPI,RF预测性能最优（Kendall>0.898,MAE<0.05）;对6、9个月尺度SPI,SVR预测性能最优（Kendall>0.95,MAE<0.04）。（3） 模型预测性能稳定性存在区别,RF预测稳定性最高,其次为SVR。（4） 构建的三种模型表现异同主要是因为SVR转为凸优化问题解决了WNN易陷入局部最优解的不足,从而提高了模型预测性能,RF集成多样化回归树,降低了弱学习器的负面影响,提高了模型预测准确率及稳定性,同时,RF处理包含噪声的降水数据的能力更强。 相似文献
6.
古尔班通古特沙漠是中国第二大沙漠,也是中国固定和半固定沙丘主要分布区,固沙灌木种较多。冠幅不仅是反映固沙灌木可视化的重要参数,也是反映沙漠植被生长情况的重要变量。以3种沙丘（固定沙丘、半固定沙丘和流动沙丘）上主要固沙灌木为研究对象,利用12种基础模型、BP(Backpropagation Neural Network)神经网络和支持向量机（Support Vector Machine,SVM）机器学习算法建立了基于固沙灌木株高和冠长率的冠幅预测模型,同时将两种机器学习算法拟合结果与基础模型进行比较,最终选出了适合研究区的冠幅预测模型。结果表明：（1）不同沙丘类型和不同灌木种类的最优冠幅预测模型不同,且固定沙丘和半固定沙丘模型优于流动沙丘。3种沙丘类型最优拟合为M2(Quadratic Model)模型;（2）白梭梭（Haloxylon persicum）在半固定沙丘和流动沙丘上拟合的最优模型分别为M2、M7(Gompertz),沙拐枣（Calligonum mongolicum）最优模型为M2,蛇麻黄（Ephedra distachya）和油蒿（Artemisia ordosica）在... 相似文献
7.
Seabed sediment textural parameters such as mud, sand and gravel content can be useful surrogates for predicting patterns of benthic biodiversity. Multibeam sonar mapping can provide near-complete spatial coverage of high-resolution bathymetry and backscatter data that are useful in predicting sediment parameters. Multibeam acoustic data collected across a ～1000 km 2 area of the Carnarvon Shelf, Western Australia, were used in a predictive modelling approach to map eight seabed sediment parameters. Four machine learning models were used for the predictive modelling: boosted decision tree, random forest decision tree, support vector machine and generalised regression neural network. The results indicate overall satisfactory statistical performance, especially for %Mud, %Sand, Sorting, Skewness and Mean Grain Size. The study also demonstrates that predictive modelling using the combination of machine learning models has provided the ability to generate prediction uncertainty maps. However, the single models were shown to have overall better prediction performance than the combined models. Another important finding was that choosing an appropriate set of explanatory variables, through a manual feature selection process, was a critical step for optimising model performance. In addition, machine learning models were able to identify important explanatory variables, which are useful in identifying underlying environmental processes and checking predictions against the existing knowledge of the study area. The sediment prediction maps obtained in this study provide reliable coverage of key physical variables that will be incorporated into the analysis of covariance of physical and biological data for this area. 相似文献
8.
Along with the gradually accelerated urbanization process, simulating and predicting the future pattern of the city is of great importance to the prediction and prevention of some environmental, economic and urban issues. Previous studies have generally integrated traditional machine learning with cellular automaton (CA) models to simulate urban development. Nevertheless, difficulties still exist in the process of obtaining more accurate results with CA models; such difficulties are mainly due to the insufficient consideration of neighborhood effects during urban transition rule mining. In this paper, we used an effective deep learning method, named convolution neural network for united mining (UMCNN), to solve the problem. UMCNN has substantial potential to get neighborhood information from its receptive field. Thus, a novel CA model coupled with UMCNN and Markov chain was designed to improve the performance of simulating urban expansion processes. Choosing the Pearl River Delta of China as the study area, we excavate the driving factors and the transformational relations revealed by the urban land-use patterns in 2000, 2005 and 2010 and further simulate the urban expansion status in 2020 and 2030. Additionally, three traditional machine-learning-based CA models (LR, ANN and RFA) are built to attest the practicality of the proposed model. In the comparison, the proposed method reaches the highest simulation accuracy and landscape index similarity. The predicted urban expansion results reveal that the economy will continue to be the primary factor in the study area from 2010 to 2030. The proposed model can serve as guidance in urban planning and government decision-making. 相似文献
9.
准确地模拟干旱区潜在蒸发量,对区域水资源的合理开发利用与生态环境保护具有十分重要的意义。以极限学习机（ELM）模型为基础,以古浪河流域的乌鞘岭、古浪两个典型气象观测站点为对象,将气象因子的不同组合作为输入参数,构建了适合当地的潜在蒸发量模型。利用构建的模型对乌鞘岭、古浪气象观测站点的月潜在蒸发量进行了模拟,将模拟结果与支持向量机（SVM）模型模拟结果进行了对比,发现ELM模型在干旱区月潜在蒸发量模拟中有更好的适用性,可为干旱地区潜在蒸发量的估算提供新方法和思路,是资料有限条件下潜在蒸发估算的有效方法。 相似文献
10.
为识别区域土壤重金属的空间变异特征并厘清其影响因素,本研究构建了多元线性回归（MLR）、弹性网络回归（ENR）、随机森林（RF）、随机梯度提升（SGB）、堆叠（stacking）集成模型、反向传播神经网络（BP-ANN）、基于模型平均的神经网络集成（avNNet）、线性核支持向量机（SVM-L）和高斯核支持向量机（SVM-R）共九种机器学习模型,利用山东省中部土壤重金属（Cd、Cu、Hg、Pb和Zn）和环境辅助变量数据,开展区域土壤重金属空间预测精度比较研究。结果表明：RF对五种重金属空间预测的决定系数（ R2）介于0.263~0.448之间,平均绝对误差（ MAE）和均方根误差（ RMSE）分别小于8.408和10.636,预测值/实际值（ P/ O）均接近于1,对五种重金属的预测效果均较为理想,是研究区土壤重金属空间预测的最优模型;SVM-R整体预测性能仅次于RF,各项精度评价指标均相对稳健,可作为备选模型;其余七种模型的预测性能均明显低于RF和SVM-R。RF的空间预测结果显示,研究区五种重金属呈现出相似的空间分布格局,含量均由研究区东北部向西南部递减,包括东北部、北部和南部3个高值区,且高值区与当地工业–交通密集区的分布格局一致,反映出人类活动是研究区土壤重金属空间分异的主要影响因素。本研究可为区域土壤污染调查、评价和管控提供科学参考。 相似文献
11.
Rockburst is a common dynamic geological hazard, severely restricting the development and utilization of underground space and resources. As the depth of excavation and mining increases, rockburst tends to occur frequently. Hence, it is necessary to carry out a study on rockburst prediction. Due to the nonlinear relationship between rockburst and its influencing factors, artificial intelligence was introduced. However, the collected data were typically imbalanced. Single algorithms trained by such data have low recognition for minority classes. In order to handle the problem, this paper employed stacking technique of ensemble learning to establish rockburst prediction models. In total, 246 sets of data were collected. In the preprocessing stage, three data mining techniques including principal component analysis, local outlier factor and expectation maximization algorithm were used for dimension reduction, outlier detection and outlier substitution, respectively. Then, the pre-processed data were split into a training set (75%) and a test set (25%) with stratified sampling. Based on the four classical single intelligent algorithms, namely k-nearest neighbors (KNN), support vector machine (SVM), deep neural network (DNN) and recurrent neural network (RNN), four ensemble models (KNN–RNN, SVM–RNN, DNN–RNN and KNN–SVM–DNN–RNN) were built by stacking technique of ensemble learning. The prediction performance of eight models was evaluated, and the differences between single models and ensemble models were analyzed. Additionally, a sensitivity analysis was conducted, revealing the importance of input variables on the models. Finally, the impact of class imbalance on the prediction accuracy and fitting effect of models was quantitatively discussed. The results showed that stacking technique of ensemble learning provides a new and promising way for rockburst prediction, which exhibits unique advantages especially when using imbalanced data. 相似文献
12.
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. 相似文献
13.
精准刻画城市住宅地价分布特征,对于科学引导城市空间布局规划、有效实现城市精明增长等具有重要意义。而城市住宅地价与其潜在影响因素之间的复杂非线性关系,给地价分布精细模拟带来了挑战。论文旨在探索基于地理大数据和集成学习的城市住宅地价分布模拟方法体系,以满足快速、精准监测地价动态变化的需要。选取武汉市为典型区,以住宅用地交易样点、兴趣点(points of interest, POI)和夜间灯光影像为数据源,以500 m分辨率网格为估价单元,提取POI核密度和夜间灯光强度作为住宅地价预测变量,采用机器学习算法和bagging、stacking集成方法构建住宅地价预测模型,并对比分析其精度。研究发现：① 单个机器学习算法中,支持向量回归(support vector regression, SVR)预测精度最高,接下来依次是 k最近邻算法( k-nearest neighbor algorithm, k-NN)、高斯过程回归(Gaussian process regression, GPR)和BP神经网络(back propagation neural networks, BP-NN);② 在提升单个算法预测精度方面,stacking方法的性能优于bagging方法,使用stacking集成SVR和 k-NN的地价预测模型精度最高,其平均绝对百分误差仅为8.29%,拟合优度 R2达0.814;③ 基于论文所构建模型生成的城市住宅地价分布图能有效表征价格圈层分布特征和局部奇异性。研究结果可为城市住宅地价评估提供新的思路和方法借鉴。 相似文献
14.
Air over-pressure (AOp) is one of the products of blasting operations for rock fragmentation in open-pit mines. It can cause structural vibration, smash glass doors, adversely affect the surrounding environment, and even be fatal to humans. To assess its dangerous effects, seven artificial intelligence (AI) methods for predicting specific blast-induced AOp have been applied and compared in this study. The seven methods include random forest, support vector regression, Gaussian process, Bayesian additive regression trees, boosted regression trees, k-nearest neighbors, and artificial neural network (ANN). An empirical technique was also used to compare with AI models. The degree of complexity and the performance of the models were compared with each other to find the optimal model for predicting blast-induced AOp. The Deo Nai open-pit coal mine (Vietnam) was selected as a case study where 113 blasting events have been recorded. Indicators used for evaluating model performances include the root-mean-square error (RMSE), determination coefficient (R2), and mean absolute error (MAE). The results indicate that AI techniques provide better performance than the empirical method. Although the relevance of the empirical approach was acceptable (R2?=?0.930) in this study, its error (RMSE?=?7.514) is highly significant to guarantee the safety of the surrounding environment. In contrast, the AI models offer much higher accuracies. Of the seven AI models, ANN was the most dominant model based on RMSE, R2, and MAE. This study demonstrated that AI techniques are excellent for predicting blast-induced AOp in open-pit mines. These techniques are useful for blasters and managers in controlling undesirable effects of blasting operations on the surrounding environment. 相似文献
15.
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。 相似文献
16.
In machine learning, one often assumes the data are independent when evaluating model performance. However, this rarely holds in practice. Geographic information datasets are an example where the data points have stronger dependencies among each other the closer they are geographically. This phenomenon known as spatial autocorrelation (SAC) causes the standard cross validation (CV) methods to produce optimistically biased prediction performance estimates for spatial models, which can result in increased costs and accidents in practical applications. To overcome this problem, we propose a modified version of the CV method called spatial k-fold cross validation (SKCV), which provides a useful estimate for model prediction performance without optimistic bias due to SAC. We test SKCV with three real-world cases involving open natural data showing that the estimates produced by the ordinary CV are up to 40% more optimistic than those of SKCV. Both regression and classification cases are considered in our experiments. In addition, we will show how the SKCV method can be applied as a criterion for selecting data sampling density for new research area. 相似文献
17.
土壤粒径（砂粒、粉粒和黏粒）是各种陆表过程和生态系统服务评估等模型的关键参数。作为一种土壤成分数据,土壤粒径的空间预测方法有和为1（或100%）等特殊要求,其空间分布精度受预测方法影响较大。本文针对土壤粒径相较于其他土壤属性的特殊性,提出了土壤粒径空间预测方法框架,综述了土壤粒径数据变换、空间插值和精度验证等系列方法,总结了提升土壤粒径空间预测精度的各种途径,包括通过有效的数据变换改善数据分布、结合数据分布特点选择合适的预测方法、结合辅助变量提升制图精度和分布合理性、使用混合模型提升插值精度、使用多成分联合模拟模型提升预测的系统性等。最后,提出了今后土壤粒径空间预测方法研究的未来方向,包括从考虑数据变换原理和机制角度改善数据分布、发展多成分联合模拟模型和高精度曲面建模方法,以及引入土壤粒径函数曲线并与随机模拟结合等。 相似文献
18.
开展干旱预测是有效应对干旱风险的前提基础,根据1960-2016年三江平原7个站点逐日降水和气温数据,利用ARIMA和ANN模型对不同时间尺度标准化降水蒸散指数（SPEI）序列进行分析建模预测。借助相关系数R、纳什效率系数NSE、Kendall秩相关系数τ、均方误差MSE和Kolmogorov-Smirnov （K-S）检验对模型的有效性进行了判定,然后分别用ARIMA和ANN模型进行12步预测,并将预测值与实际值进行比较。结果表明：（1） ARIMA模型和ANN模型对SPEI的预测能力都随时间尺度的增加而逐渐提高。（2）两种模型对3、6个月尺度SPEI的预测精度偏低,9、12、24个月的SPEI的预测精度在70%以上;（3）SPEI-9、SPEI-12、SPEI-24三个时间尺度ANN模型的预测精度优于ARIMA模型。 相似文献
19.
自然灾害的预测预报被认为是主动减灾防灾研究中较为经济有效的方式,其中,滑坡空间预测是滑坡灾害研究的基础工作。以汶川地震重灾区北川县为研究区,选取坡度、高程、岩石类型、地震烈度、水系、道路等6个重要滑坡影响因素作为评价因子,全面分析了地震滑坡分布与各影响因子之间的统计相关性,分别采用多元回归模型与神经网络模型计算滑坡灾害敏感性指数,并进行分级和制图。结果表明,极高和高敏感区主要分布在曲山、陈家坝等乡镇,主要沿着龙门山断裂带周边地区的河流和道路呈带状分布。其中,回归模型的预测精度为73.7%,神经网络模型的预测精度为81.28%,在本区域内,神经网络模型在滑坡灾害空间预测方面更具优势。 相似文献
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