基于小波支持向量机模型的矿区生态安全评价方法研究

doi:10.11872/j.issn.1005-2518.2020.06.007

Abstract

Abstract:

In the primary stage of mining area development，economic growth should be realized by relying on resource endowment of mining area.The high consumption of resources and energy and the high input of production factors give rise to the huge cost of environmental damage in the mining area，the pollution problems are evolving from simple one-way to complex ones.The system of compensation for ecological and environ-mental damages is conducive to solving the law enforcement dilemma of “corporate pollutes， residents victims， government pays”，and helping China’s ecological and environmental protection to the depth of development.However，ecological safety evaluation can provide a basis for the diagnosis of ecological environment quality and compensation for damages in mining areas，and they are also an important part of high-quality development of mining area economy.Aiming at the low accuracy of themining ecological security evaluation，the index system for mine ecological security evaluation was designed，including mining environment quality，pollutant discharge or emission，ecological protection，carrying capacity and the coordination of ecological system.Furthermore，20 secondary indexes were set，such as vegetation coverage rate，pollution source diffusion rate，land reclamation rate and so on.The quasi-optimal classification hyper-plane was constructed based on selected kernel function to combine Wavelet Theory with Support Vector Machines，and an integer hybrid model was established.The evaluation model was applied to the ‘G’ rare earth mining area，and the results show that there are 10 samples with grade Ⅰ risk level，that is，their environment are poor，and there are 8 samples with grade Ⅱ risk level，that is，the environment is poorer.Compared with results by GIS identifying，the error rate of the model is 5%，which has better warning accuracy.Further，compared with BP neural network，ARIMA and other methods commonly used at present，the proposed model has well-forecasting performance，can represent the nonlinear time series well.So，it has a good application prospect in the field of nonlinear time series.

Key words: mining area, ecological security, evaluation model, Wavelet Support Vector Machines（WSVM）, Kernel function, GIS

CLC Number:

X822

Jiyu TAN,Gaochang LIU. Ecological Security Evaluation of Mining Area Based on WSVM[J].Gold Science and Technology, 2020, 28(6): 902-909.

Figures/Tables 6

Fig.1

Table 1

Some common kernel types"

核函数类型	表达式	参数取值
线性核	$k (x i, x j) = x i T x j$
多项式核	$k (x i, x j) = (x i T x j) d$	$d ≥ 1$
拉普拉斯核	$k (x i, x j) = e x p (- ‖ x i - x j ‖ 2 2 σ 2)$	$σ > 0$
高斯核	$k (x i, x j) = e x p (- ‖ x i - x j ‖ σ)$	$σ > 0$
Sigmoid核	$k (x i, x j) = t a n h (β x i T x j + θ)$	$β > 0, θ < 0$

Table 1

Table 2

Fig.2

Table 3

Fig.3

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指标	指标值					目标值	指标性质
指标	2011年	2012年	2013年	2014年	2015年	目标值	指标性质
X₁₁	15.32	14.70	18.62	20.78	21.66	23.04	（+）
X₁₂	11.12	7.25	8.75	16.22	18.03	25.00	（+）
X₁₃	8.06	6.03	10.58	23.06	21.30	18.00	（+）
X₁₄	50.00	42.00	54.00	65.00	76.00	90.00	（+）
X₁₅	21.50	23.30	12.50	12.30	9.70	5.00	（-）
X₁₆	22.40	25.30	17.90	17.50	15.70	15.00	（-）
X₂₁	12.90	13.00	18.00	18.60	18.80	15.00	（+）
X₂₂	60.80	60.20	65.70	68.30	70.60	90.00	（+）
X₂₃	7.00	12.00	11.00	8.00	5.00	2.00	（-）
X₃₁	2.00	1.00	6.00	6.00	7.00	10.00	（+）
X₃₂	6.00	4.00	8.00	11.00	15.00	27.00	（+）
X₃₃	65.00	67.00	75.00	77.00	87.00	90.00	（+）
X₃₄	1.50	0.30	2.00	2.20	2.20	3.00	（+）
X₄₁	90.00	85.00	85.00	85.00	80.00	80.00	（+）
X₄₂	17.00	17.00	20.00	20.00	30.00	50.00	（+）
X₄₃	1.20	0.60	1.20	1.50	1.80	2.00	（+）
X₄₄	2.00	4.00	4.00	4.00	5.00	5.00	（-）
X₅₁	23.50	26.70	38.50	44.80	60.40	65.00	（+）
X₅₂	65.00	50.00	65.00	75.00	78.00	80.00	（+）
X₅₃	55.00	48.00	60.00	72.00	75.00	70.00	（+）

对比项	预测值	目标值	对比项	预测值	目标值
X₁₁	22	23	X₃₂	15	27
X₁₂	20	25	X₃₃	90	90
X₁₃	25	18	X₃₄	3	3
X₁₄	86	90	X₄₁	80	80
X₁₅	9	5	X₄₂	40	50
X₁₆	14	15	X₄₃	1.8	2
X₂₁	22	15	X₄₄	5	5
X₂₂	80	90	X₅₁	61	65
X₂₃	5	2	X₅₂	78	80
X₃₁	7	10	X₅₃	77	70

Ecological Security Evaluation of Mining Area Based on WSVM

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