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基于机器学习的西南岩溶泉流量模拟研究
Modelling karst spring flow in Southwest China based on machine learning

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马从文 1   张志才 1 *   陈喜 2   程勤波 1   彭韬 3,4   张林 3,4  
文摘 岩溶泉对西南岩溶区生态系统稳定和经济社会发展具有重要意义。受岩溶区独特水文地质结构与多重水流过程控制,岩溶泉流量具有复杂的动态变化特征,机器学习模型为其模拟和预测提供了有效手段。然而,岩溶泉域降雨-泉流量过程及其时空变异特征对机器学习模型结构与模拟精度的影响仍不明晰。本文选取西南典型岩溶泉,基于长短期记忆网络(LSTM)建立岩溶泉流量模拟模型,利用泉域实测逐小时降雨与泉流量序列进行模型训练与验证。在此基础上,分析了不同降雨-泉流量过程对岩溶泉流量模拟精度的影响,以及岩溶水文地质结构对降雨-泉流量响应滞时的控制作用。研究结果显示,山坡岩溶泉与流域出口岩溶泉训练期纳什效率系数(NSE)分别为0.942与0.951,验证期分别为0.831与0.834。对于山坡岩溶泉与流域出口岩溶泉,利用全年实测序列训练的模型预测雨季泉流量存在较大偏差, NSE分别为0.793与0.798,而利用雨季实测序列训练的模型预测雨季泉流量,精度显著提升,NSE分别为0.956与0.962,且此差异在暴雨频繁的5、6、7月尤为显著。受浅薄土壤与表层岩溶带分布影响,山坡岩溶泉LSTM模型时序步长显著小于流域出口岩溶泉。
其他语种文摘 Karst springs are important for ecosystem and economic development in Southwest China. Controlled by the unique karst hydrogeological structure and multiple water flow processes, the karst spring flow has complex dynamic characteristics, thus posting a great challenge to simulate and predict the dynamic process of karst spring flow which can reflect the characteristics of rainfall -spring flow in the karst basin. As a data-driven model, the machine learning model omits the necessity of considering complex physical processes, showing its significant advantages in the simulation and prediction of nonlinear system variables. Therefore, it provides an effective approach for simulation and prediction of karst spring discharge. However, the influence of the flow processes and hydrogeological conditions on the structure and simulation accuracy of machine learning model is still unclear. Among the machine learning algorithms, LSTM, as the most popular algorithm in recent years, is widely used in the simulation and prediction of various long-time series data. LSTM adds a cell state similar to "conveyor belt" in the hidden layer, and the cell state is adjusted by forgetting gate, input gate and output gate. This structure can effectively solve the long-term transportation and memory problems of time series data, and is more suitable for runoff simulation and prediction than the traditional neural network algorithm. In this study, for the processes of rainfall-spring flow in karst areas, two typical karst springs (hillside karst spring and outlet karst spring) representing different geomorphic units in Southwest China are selected. Through hyper parameter optimization, a double hidden layer and double input LSTM model are adopted to build a machine learning model of typical karst spring flow. The measured meteorological and hydrological data from 0:00 on January 1, 2017 to 24:00 on December 31, 2019 are used. 2017 -2018 is the training period and 2019 is the validation period. The model has been trained and verified. Based on the simulation results, the influence of different rainfall -spring flow forming processes on the simulation accuracy of karst spring flow and the influence of karst hydrogeological structure on the response time lag of rainfall-spring flow are compared and analyzed. The results show that the Nash efficiency coefficients (NSE) for the hillside karst spring and the outlet karst spring were 0.942 and 0.951 in the training period, and 0.831 and 0.834 in the validation period, respectively. The model can well simulate the whole dynamic process of karst spring discharge in different geomorphic units, but there are significant errors in the simulation of flood peak in the rainy season. The formation process and variation of rainfall -spring discharge in the karst spring area have an important influence on the simulation accuracy of machine learning model. Compared with the model trained by the annual measured sequence, the model trained by the measured sequence in the rainy season can significantly improve the simulation accuracy of the karst spring flow in this season. The NSE of the hillside karst spring increases from 0.793 to 0.956, and the outlet karst spring increases from 0.798 to 0.962. The difference is most significant in May, June and July when rainstorms and flood are concentrated. Under the same precision of simulating spring flow, the time step of LSTM model of hillside karst spring is obviously smaller than that of outlet karst spring. When the simulation precision of the two types of spring flow is the highest, the time step of the model is 15 h and 28 h, respectively. This result can be combined with the actual geological structure. In karst areas, the development degree of the epikarst zone is closely related to the terrain, and its thickness usually decreases with the increase of the slope.
来源 中国岩溶 ,2024,43(1):48-56 【扩展库】
DOI 10.11932/karst2023y013
关键词 机器学习 ; LSTM ; 岩溶泉流量 ; 响应滞时 ; 岩溶降雨-泉流量过程
地址

1. 河海大学水文水资源学院, 江苏, 南京, 210098  

2. 天津大学地球系统科学学院, 天津, 300072  

3. 中国科学院地球化学研究所, 贵州, 贵阳, 550081  

4. 中国科学院普定喀斯特生态系统观测研究站, 中国科学院普定喀斯特生态系统观测研究站, 贵州, 普定, 562100

语种 中文
文献类型 研究性论文
ISSN 1001-4810
学科 地质学
基金 国家自然科学基金 ;  面上项目
文献收藏号 CSCD:7723572

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引证文献 1 篇

1 徐子凡 表层岩溶带土岩结构对降雨-径流响应特征的影响 中国岩溶,2024,43(4):863-875
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