Research on water resources carrying capacity of shale gas development area based on GA-BP neural network
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摘要: 以西南地区威远县为例,从社会、经济、生态、水资源和页岩气开发5个方面构建水资源承载力评价指标体系及分级标准,采用遗传算法(genetic algorithm,GA)优化BP (back propagation)神经网络,形成GA-BP神经网络组合模型,对研究区2014—2019年的水资源承载力状态进行评价。结果表明:使用GA-BP神经网络计算得到验证数据的最大相对误差为6.5%,期望输出与结果的相关系数为0.995 98。随着页岩气井群规模的增大,研究区水资源承载力指数总体上逐年降低,其中2014—2017年水资源承载力为可承载状态,2018—2019年为弱承载状态;水资源承载力指数主要影响指标为人均水资源量、页岩气的井群规模和万元工业增加值用水量等。Abstract: Taking Weiyuan County in Southwest China as an example, the evaluation system and grading standard of water resources carrying capacity were constructed from five aspects, i.e. society, economy, ecology, water resources and shale gas development, and the genetic algorithm (GA) was used to optimize back propagation (BP) neural network. GA-BP neural network combined model was thus formed to evaluate the water resources carrying capacity status of the study area from 2014 to 2019. The results showed that the maximum relative error of the verification data calculated by GA-BP neural network was 6.5%, and the correlation coefficient between the expected output and the result was 0.995 98. With the increase in the scale of shale gas well groups, the water resources carrying capacity index of the study area had been decreased year by year. The water resources carrying capacity from 2014 to 2017 was in a bearable state, and from 2018 to 2019, it was in a weakly carrying state. The main impact indicators of the index were per capita water resources, shale gas well group scale and water consumption of 10 000 yuan of industrial added value.
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Key words:
- shale gas /
- water resources carrying capacity /
- genetic algorithm /
- BP neural network
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