| 基于机器学习模型的沙漠腹地地下水含盐量变化过程及模拟研究 |
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| 作者姓名： | 范敬龙 刘海龙 雷加强 徐新文 王桂芬 钟显斌 闫健 |
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| 基金项目： | 中国科学院西部博士专项(XBBS200904);国家自然科学基金项目(41030530,41271341);塔里木油田科技项目(971012080007) |
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| 摘 要： | 为了研究塔克拉玛干沙漠腹地的地下水盐分变化规律,模拟地下水盐分变化过程,评价适合该区域的地下水变化规律的模型。通过对研究区蒸发量、降水量、气温、气压、地下水位、地下水电导率数据的统计分析,揭示了地下水含盐量及其影响因素的特征;使用GP模型、GPLVM模型和BP人工神经网络模型以及综合模型,模拟了气候变化和人类活动双重影响下的地下水含盐量变化过程,并评价了模型的模拟结果。研究结果表明:(1)研究区地下水流动系统主要受气候变化和人类活动的影响,地下水位在局部地区随开采过程呈现波动变化。地下水位变化过程与气压的变化规律相一致;而气温和蒸发量的季节变化规律相一致。地下水盐分含量呈上升趋势。(2)GP模型对于地下水含盐量的预测效果最好;GPLVM模型对于已知地下水含盐量条件下,与其他环境因素进行多元回归分析的拟合效果最好。而GP、GPLVM和BP人工神经网络模型的综合模型,对于包括模型训练和模型预测的全体数据集的拟合和预测效果最好。
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| 关 键 词： | 地下水含盐量 高斯过程 高斯过程隐变量模型 人工神经网络 沙漠腹地 |
Groundwater salt content change and its simulation based on machine learning model in hinterlands of Taklimakan Desert |
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| Authors: | FAN Jinglong LIU Hailong LEI Jiaqiang XU Xinwen WANG Guifen ZHONG Xianbin and YAN Jian |
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| Abstract: | The Taklimakan Desert, in the center of the Tarim Basin, Northwest China, is near the arid center of the Eurasian Continent. It is an ideal place for carrying out research on circulation systems of ocean-continent and continent-continent, as well as on the environmental effects of the Tibetan Plateau uplift. Oil and natural gas resources are rich in the desert hinterlands, where weather is extremely dry and the ecological environment is extremely frail. How to achieve sustainable development of oil gas energy exploitation and water resources will become a key issue of rapid and sustained energy development in the exploitation area. Since 2000, with the increase of oil and gas production, as well as construction of ecological engineering in oil field areas, regional water use, i.e., water consumption by oil exploitation, life-service water, and ecological engineering water has been increasing. Under this condition, the response of regional groundwater to water-use behavior, and sustainable use of groundwater under scaled development of future oil gas energy, is a focus of discussion in academic circles. All the water used for oil field production, living, and an ecological shelterbelt program in the Tarim oil field was supplied by 19 local water wells. The main groundwater was from surrounding snow cover and glacier meltwater that leak into the groundwater system at a mountain pass, which then inflows slowly as runoff with terrain change. There is a single chemical type of groundwater in the study area, which is mainly alkali and a strong acid ion. Groundwater is an important resource within desert areas, for both human use and maintenance of the ecological environment. However, little information is available on the long-term change and evolutionary trends of groundwater salt content in the region. The aim of this study is to increase knowledge of such content and its influences there. We simulated this content, studied its change, and evaluated the effects and applicability of machine learning models for the hinterland of Taklimakan Desert. For these purposes, three machine learning models and an appropriate integrated model were designed to simulate the variation of groundwater salt content. Based on statistical analysis of evaporation, precipitation, air temperature, air pressure, groundwater level and conductivity, this paper reports some statistical features of that content and its influences. The temporal change of content was simulated using Gaussian Process (GP), Gaussian Process Latent Variable Model (GPLVM), Back Propagation Artificial Neural Network (BP-ANN), and an integrated machine learning model, under the impacts of climate change and human activities. The modeling was evaluated using root mean square error (RMSE) and uncentered R-squared (uncentered R2).The results show that the groundwater flow system is affected by climate change and human activities, and groundwater level appears steady with small fluctuation in the study area. The change of groundwater level was concordant with air pressure, and the change of air temperature was consistent with evaporation. There was an overall uptrend of groundwater salt content. Accurate prediction can be achieved with GP, whereas GPLVM was the better multiple regression method for a known groundwater salt content among the three models. The integrated model of GP, GPLVM, and BP-ANN gave the best results for all observation datasets, including training and prediction data. The results not only help clarify regional hydrologic cycle processes under extremely dry conditions, but can also provide appropriate theoretical evidence for sustainable utilization of water resources and safe production in desert oilfields. |
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| Keywords: | groundwater salt content gaussian process gaussian process latent variable model back propagation artificial neural network hinterlands of taklimakan desert |
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