|
| 基于人工神经网络模型的沧州市水生态承载力评估 |
| Assessment of water ecological carrying capacity in Cangzhou City based on artificial neural network model |
| Received:October 24, 2024 |
| DOI：10.13254/j.jare.2024.0837 |
| 中文关键词: 水生态承载力;指标体系;人工神经网络;沧州市 |
| 英文关键词: water ecological carrying capacity;index system;artificial neural network;Cangzhou City |
| 基金项目:沧州市水环境质量改善技术服务项目 |
| Author Name | Affiliation | E-mail | | ZHENG Haowei | State Key Laboratory of Environmental Criteria and Risk Assessment, State Environmental Protection Key Laboratory for Lake Pollution Control, National Engineering Laboratory for Lake Pollution Control and Ecological Restoration, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
China National Gold Group Co., Ltd, Beijing 100010, China | | | WANG Huanhua | State Key Laboratory of Environmental Criteria and Risk Assessment, State Environmental Protection Key Laboratory for Lake Pollution Control, National Engineering Laboratory for Lake Pollution Control and Ecological Restoration, Chinese Research Academy of Environmental Sciences, Beijing 100012, China | | | LU Shaoyong | State Key Laboratory of Environmental Criteria and Risk Assessment, State Environmental Protection Key Laboratory for Lake Pollution Control, National Engineering Laboratory for Lake Pollution Control and Ecological Restoration, Chinese Research Academy of Environmental Sciences, Beijing 100012, China | lushy2000@163.com | | JIA Jianli | School of Chemical and Environmental Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China | | | WAN Zhengfen | State Key Laboratory of Environmental Criteria and Risk Assessment, State Environmental Protection Key Laboratory for Lake Pollution Control, National Engineering Laboratory for Lake Pollution Control and Ecological Restoration, Chinese Research Academy of Environmental Sciences, Beijing 100012, China | | | BI Bin | State Key Laboratory of Environmental Criteria and Risk Assessment, State Environmental Protection Key Laboratory for Lake Pollution Control, National Engineering Laboratory for Lake Pollution Control and Ecological Restoration, Chinese Research Academy of Environmental Sciences, Beijing 100012, China | | | ZHANG Senlin | State Key Laboratory of Environmental Criteria and Risk Assessment, State Environmental Protection Key Laboratory for Lake Pollution Control, National Engineering Laboratory for Lake Pollution Control and Ecological Restoration, Chinese Research Academy of Environmental Sciences, Beijing 100012, China | |
|
| Hits: 1013 |
| Download times: 1193 |
| 中文摘要: |
| 水生态承载力研究可作为从水生态角度预判可持续发展中产生问题的有效手段，但水生态承载力因其指标概念不统一，且需多领域交叉，传统量化过程繁琐复杂。本研究通过构建水资源、水环境、水生态和水安全的指标体系，结合人工神经网络的结构和特点，构建水生态承载力的量化评价模型，可为水生态承载力计算提供一种科学方法，并以典型生态缺水型城市河北沧州市为例进行了验证。本研究通过对沧州市2015—2019年数据进行计算，得到逐年水生态承载力指数分别为0.26、0.23、0.23、0.24、0.22，反映出其生态承载力整体处于及格水平，可能威胁到生态环境绿色健康可持续发展。可见，指标体系和神经网络模型结合，具有结构简单、抗干扰能力强、结果更直观等优点，使区域承载状态更直观有效地呈现出来，为该区域的水生态问题解决和未来发展方向的指定提供帮助。 |
| 英文摘要: |
| The study of water ecological carrying capacity can be used as an effective approach to predict the problems arising in sustainable development from the perspective of water ecology. The traditional quantitative process of water ecological carrying capacity is complicated due to the index concept inconformity and multi-field intersection. In this study, the quantitative evaluation model of water ecological carrying capacity was established by constructing the index system of water resources, water environment, water ecology and water security, and combining the structure and characteristics of artificial neural network, which provides a scientific method for the calculation of water ecological carrying capacity. Moreover, taking Cangzhou City, a typical eco-water-deficient city, as an example, the calculations showed that from 2015 to 2019 the indexes of water ecological carrying capacity were 0.26, 0.23, 0.23, 0.24 and 0.22, respectively. It reflected that the overall ecological carrying capacity was at a loaded state, barely meeting the standard level, which may threaten the green, healthy, and sustainable development of the ecological environment. It can be seen that the combination of the index system and neural network model has the advantages of simple structure, strong anti-interference ability and more intuitive results, so that the regional carrying capacity state can be presented more intuitively and effectively, providing help for the solution of water ecological problems in this region and the designation for future development direction. |
| HTML
View Full Text
View/Add Comment Download reader |
| Close |
|
|
|
