地下水潜力填图与评价的混合人工智能模型开发与应用

IF 2.4 Q2 GEOSCIENCES, MULTIDISCIPLINARY VIETNAM JOURNAL OF EARTH SCIENCES Pub Date : 2022-06-24 DOI:10.15625/2615-9783/17240
Duong Hai Ha, Huong Thi Thanh Ngo, Phong Tran Van, Dam Nguyen Duc, Mohammadtaghi Avand, Duy Nguyen Huu, M. Amiri, Hiep Van Le, Indra Prakash, Pham Binh Thai
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引用次数: 3

摘要

地下水潜力评估对于地下水资源的最佳利用和补给,对一个地区的适当开发和管理至关重要。本研究的主要目的是使用混合人工智能模型开发准确的越南大农省地下水潜力图,该模型是随机森林(RF)及其集成框架(AdaBoost-ABRF、Bagging-BRF和LogitBoost-LBRF)的组合。在这项研究中,使用了12个条件因素,即地形(纵横比、海拔、地形湿度指数-TVI、坡度和曲率)、水文(渗透和河流密度、降雨量、沉积物传输指数-STI、水流功率指数-SPI)、土地利用和土壤来开发模型。产量数据也被用于开发和验证潜在的地下水区域。利用一条规则(R)特征选择方法对地下水潜力影响参数的重要性进行优先级排序。结果表明,降雨因子的平均Merit(AM)最高(68.039),河流密度最低(53969)。ML模型的性能评估使用标准统计指标进行,包括受试者工作特征下面积(ROC)曲线(AUC)。结果表明,四个模型在训练(AUC≥0.967)和测试(AUC≤0.734)阶段都表现良好,但ABRF(AUC=0.992)模型在训练阶段的表现最好,而LBRF在测试阶段是最好的(AUC=0.76)。本模型研究将有助于正确评估地下水潜力和管理地下水资源以促进可持续发展。
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Development and application of hybrid artificial intelligence models for groundwater potential mapping and assessment
Groundwater potential assessment is essential for optimum utilization and recharge of groundwater resources for the proper development and management of an area. The main aim of this study is to develop an accurate groundwater potential map of the Dak Nong Province (Vietnam) using hybrid artificial intelligence models, which are a combination of Random Forest (RF) and its Ensemble Framework (AdaBoost - ABRF, Bagging - BRF and LogitBoost - LBRF). In this study, twelve conditioning factors, namely topography (aspect, elevation, Topographic Wetness Index - TWI, slope, and curvature), hydrology (infiltration and river density, rainfall, Sediment Transport Index - STI, Stream Power Index - SPI), land use, and soil were used to develop the models. Well, yield data was also utilized to develop and validate potential groundwater zones. One Rule (R) feature selection method was utilized to prioritize the importance of groundwater potential affecting parameters. The results indicated that the Average Merit (AM) of the rainfall factor was the highest (68.039), and river density was the lowest (53,969). Performance evaluation of ML models was done using standard statistical indicators, including Area Under the Receiver Operating Characteristic (ROC) curve (AUC). The results showed that all the four models performed well in the training (AUC ≥ 0.967) and testing (AUC ≥ 0.734) phases, but the performance of the ABRF (AUC=0.992) model is the best in the training phase, whereas LBRF is the best in the testing phase (AUC=0.776). The present model study would be helpful in the proper groundwater potential assessment and management of groundwater resources for sustainable development.  
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来源期刊
VIETNAM JOURNAL OF EARTH SCIENCES
VIETNAM JOURNAL OF EARTH SCIENCES GEOSCIENCES, MULTIDISCIPLINARY-
CiteScore
3.60
自引率
20.00%
发文量
0
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