基于农户受偿意愿的农田生态补偿额度测算——以武汉市的调查为实证

农业环境政策已成为西方发达国家激励乡村适宜景观地保护的有效方式,有利于克服农田生态环境供给的不足,鼓励农户逐渐向绿色农业、生态农业或有机农业的方向发展。以武汉市农户的调查为实证,应用意愿调查法构建模拟的农田生态补偿政策及交易市场,从减少农业负外部性行为,对农民放弃一定程度化肥、农药等化学物质的施用所带来的损失给予补偿的角度,测算出农户对农田生态环境补偿的意愿及额度。研究表明:①当化肥农药施用在减少50%、100%等不同的限制标准下,受访农户愿意生产及供给农田生态服务的人数比例在69.32%~85.25%,并认为政府应分别向农户补偿3 928.88~8 367.00元/ hm²·a,与农户按生产经验判断的减产幅度、增加的管理难度和工时相近;②从模拟的农产品交易市场出发,受访农户中愿意生产并供给化肥、农药施用量在不同限制标准下的农产品的比例在54.29%~82.12%,愿意以高出当前普通农产品1.65~2.67元/kg的价格生产环境友好型农产品,价格增幅在42.52%~68.45%。     

基于扇区动态复杂性因素的航空管制员工作负荷计算

为了更加准确地计算和预测航空管制员的工作负荷,利用雷达管制模拟试验获取的数据,分别采用线性回归、神经网络的非线性回归和基于神经网络的支持向量机方法,建立了基于扇区复杂性因素的管制员工作负荷实时计算模型。结果表明,这3种模型的绝对误差平均值分别为0.969、1.049、0.240;相对误差平均值分别为16.667%、17.979%、6.229%;均方根误差分别为0.186、0.206、0.114。另外,若采用5%作为基准精度,基于神经网络的支持向量机模型可以将相对误差控制在-0.5%~0.5%,表现出较强的误差控制能力。研究表明,可以采用扇区动态复杂性因素来计算管制员的工作负荷,相比线性回归、神经网络的非线性回归方法,基于神经网络的支持向量机方法对管制员工作负荷的计算有更高的精度。     

Development of HGAPSO-SVR corrosion prediction approach for offshore oil and gas pipelines

Managing the oil and gas pipelines against corrosion is one of the major challenges of the oil and gas sector because of the complexities associated with the initiation, stabilization, and growth of the corrosion defects. The present research attempts to develop a model for predicting the maximum depth of pitting corrosion in oil and gas pipelines using SVM algorithm. In order to improve the SVM performance, Hybrid PSO and GA was utilized. Monte Carlo simulation was used to determine the time lapse for the pit depth growth. In order to implement the above modeling approaches and to prove their efficiency and accuracy against a large database, a total of 340 data samples for corrosion depth and rate are retrieved from the Iranian Oilfields. The performance of the new algorithm shows that it has higher stability and accuracy. In addition, the forecasting results of the new algorithm are compared with the 11 intelligent optimization algorithms, it shows that the novel hybrid algorithm has higher accuracy, better generalization ability, and stronger robustness. The coefficient of determination (R²) value in the testing phase for SVM-HGAPSO was estimated by 0.99. Proposed hybrid model and Monte-Carlo simulations pitting corrosion based on Poisson square wave process have been used to predict the time evolution of the mean value of the pit depth distribution for different categories of maximum pitting rates (low, moderate, high and sever). The models was validated with 4 field data for each of the pitting corrosion categories and the results agreed well. The pipelines under severe pitting corrosion rate were, more conservatively predicted by HGAPSO-SVR than those under low, moderate and high pitting corrosion rates. The results obtained demonstrate the potentials of this technique for the integrity management of corroded aged pipelines.     

Prediction of gas explosion pressures: A machine learning algorithm based on KPCA and an optimized LSSVM

A gas explosion, as a common accident in public life and industry, poses a great threat to the safety of life and property. The determination and prediction of gas explosion pressures are greatly important for safety issues and emergency rescue after an accident occurs. Compared with traditional empirical and numerical models, machine learning models are definitely a superior approach. However, the application of machine learning in gas explosion pressure prediction has not reached its full potential. In this study, a hybrid gas explosion pressure prediction model based on kernel principal component analysis (KPCA), a least square support vector machine (LSSVM), and a gray wolf optimization (GWO) algorithm is proposed. A dataset consisting of 12 influencing factors of gas explosion pressures and 317 groups of data is constructed for developing and evaluating the KPCA-GWO-LSSVM model. The results show that the correlations among the 12 influencing factors are eliminated and dimensioned down by the KPCA method, and 5 composite indicators are obtained. The proposed KPCA-GWO-LSSVM hybrid model performs well in predicting gas explosion pressures, with coefficient of determination (R²), root mean square error (RMSE), and mean absolute error (MAE) values of 0.928, 26.234, and 12.494, respectively, for the training set; and 0.826, 25.951, and 13.964, respectively, for the test set. The proposed model outperforms the LSSVM, GWO-LSSVM, KPCA-LSSVM, beetle antennae search improved BP neural network (BAS-BPNN) models and reported empirical models. In addition, the sensitivity of influencing factors to the model is evaluated based on the constructed database, and the geometric parameters X1 and X2 of the confined structure are the most critical variables for gas explosion pressure prediction. The findings of this study can help expand the application of machine learning in gas explosion prediction and can truly benefit the treatment of gas explosion accidents.     

Water quality prediction of copper-molybdenum mining-beneficiation wastewater based on the PSO-SVR model

● Data acquisition and pre-processing for wastewater treatment were summarized. ● A PSO-SVR model for predicting COD_eff in wastewater was proposed. ● The COD_eff prediction performances of the three models in the paper were compared. ● The COD_eff prediction effects of different models in other studies were discussed. The mining-beneficiation wastewater treatment is highly complex and nonlinear. Various factors like influent quality, flow rate, pH and chemical dose, tend to restrict the effluent effectiveness of mining-beneficiation wastewater treatment. Chemical oxygen demand (COD) is a crucial indicator to measure the quality of mining-beneficiation wastewater. Predicting COD concentration accurately of mining-beneficiation wastewater after treatment is essential for achieving stable and compliant discharge. This reduces environmental risk and significantly improves the discharge quality of wastewater. This paper presents a novel AI algorithm PSO-SVR, to predict water quality. Hyperparameter optimization of our proposed model PSO-SVR, uses particle swarm optimization to improve support vector regression for COD prediction. The generalization capacity tested on out-of-distribution (OOD) data for our PSO-SVR model is strong, with the following performance metrics of root means square error (RMSE) is 1.51, mean absolute error (MAE) is 1.26, and the coefficient of determination (R²) is 0.85. We compare the performance of PSO-SVR model with back propagation neural network (BPNN) and radial basis function neural network (RBFNN) and shows it edges over in terms of the performance metrics of RMSE, MAE and R², and is the best model for COD prediction of mining-beneficiation wastewater. This is because of the less overfitting tendency of PSO-SVR compared with neural network architectures. Our proposed PSO-SVR model is optimum for the prediction of COD in copper-molybdenum mining-beneficiation wastewater treatment. In addition, PSO-SVR can be used to predict COD on a wide variety of wastewater through the process of transfer learning.     

支持向量机法在煤与瓦斯突出分析中的应用研究

通过分析采煤工作面煤与瓦斯涌出量与地质构造指标的对应关系,应用支持向量机(SVM)方法对煤与瓦斯涌出类型及涌出量进行分析。建立两类突出识别的SVM模型、多类型突出识别的H-SVMs模型以及预测瓦斯涌出量的支持向量回归模型。研究结果表明:SVM方法能够很好地对煤与瓦斯突出模式进行识别,所建立的采煤工作面瓦斯涌出量预测模型的精度高于应用BP神经网络预测精度;SVM理论基础严谨,决策函数结构简单,泛化能力强,并且决策函数中的法向量W可以反映突出模式识别的地质结构指标的权重。     

基于支持向量回归机的煤层瓦斯含量预测研究

为了对煤层瓦斯含量进行准确预测,应用支持向量回归机(SVR)理论建立煤层瓦斯含量预测模型,结合现场实测数据利用支持向量机(SVM)工具箱进行模型的求解及预测,并从均方根误差、希尔不等系数和平均绝对百分误差3个不同误差指标与人工神经网络预测模型进行比较分析。研究结果表明:SVR模型其预测精度及可行性高于神经网络模型,而且运算快,实时性较好,用于煤层瓦斯含量的预测较理想,具有良好的应用前景,可以为煤矿瓦斯防治提供理论依据。     

耕地转为建设用地价格评估——以河南省南阳市为例

以经济增长理论为基础的传统土地估价理论与方法,对耕地价值缺乏完整认识,造成了耕地价格与转用建设用地价格的不连续性,论文在土地资源总价值理论基础上,通过对耕地价值的重建、耕地转为建设用地增值空间分析以及权益归属的研究,重新构建了城乡交错带耕地转为建设用地价格评估体系,提出耕地价格是经过外部性内在化以后的耕地资源权利的价值体现,它包括耕地实际生产收益价格、社会保障价格、社会稳定价格、生态安全价格和发展权价格5部分。同时以河南省南阳市为例,对该方法进行了实际应用,得出南阳市水田单位面积价值总量价格测算结果为139.81元/m²,该评估较好地反映了耕地的转用价格。     

我国煤炭资源开采与转运的生态环境问题及对策

我国煤炭生产地与消费地呈逆向分布,形成北煤南运、西煤东运的全国煤炭输送总体格局。但是由于国家长期实行无偿调拨和煤炭低价政策,煤炭生产地区在当地生态环境遭受严重破坏的同时,经济发展水平却并没有得到快速增长,因此难以承担生态环境恢复的重任。煤炭开采所带来的生态环境问题应作为"区域性"问题,其生态恢复费用应由产地和消费地共同承担。所以,制定合理的自然资源价格政策和区域生态补偿政策是改善这些地区生态环境的重要途径。      

基于理性预期理论的大气污染经济成本评估

我国大气污染形势严峻,科学合理地评估大气污染的经济损失不仅有益于政策效益分析,同时是“绿色国民经济”核算的一项基础性工作。基于我国2013年2月到2018年7月的区县月度房屋交易数据和7种大气污染指标(AQI、SO₂、NO₂、CO、O₃、PM₁₀、PM_2.5)的浓度数据,运用特征价格模型实证量化大气污染物减少的边际支付意愿(MWTP)和总经济损失。首先通过改变理性预期的时间段验证理性预期假设的成立,在此基础上采用理性预期方法解决遗漏变量所导致的内生性问题。研究结果显示:①NO₂、CO、PM_2.5和PM₁₀每上升1μg/m³,房价分别降低约2.04%、0.028%、0.34%和0.39%;而SO₂与O₃对房价的影响并不显著。②近年来大气污染的经济损失有所降低,政府的治理效果显著,但仍不容小觑。2013年AQI、PM₁₀和PM_2.5未达标导致的经济损失分别约为35600亿元、19300亿元和24100亿元,约占当年GDP的6.06%、3.29%和4.11%;2018年分别降低至19200亿元、5300亿元和6700亿元,占当年GDP的2.14%、0.60%和0.74%。尽管PM₁₀和PM_2.5浓度也在逐年下降,但仍未达到《环境空气质量标准》所要求的二级限值。最终评估结果显示,PM₁₀和PM_2.5二者导致的经济损失的加总数值,与AQI得到的数值相差无异。进一步证实了我国当前大气污染导致的社会经济福利损失主要是来自PM₁₀和PM_2.5的超标,因此治理“雾霾”是改善当前空气质量的关键。