利用混合和集合深度学习模型探测地面沉降

IF 2.3 Q2 REMOTE SENSING
Narges Kariminejad, Aliakbar Mohammadifar, Adel Sepehr, Mohammad Kazemi Garajeh, Mahrooz Rezaei, Gloria Desir, Adolfo Quesada-Román, Hamid Gholami
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引用次数: 0

摘要

土地沉降(LS)是最突出的地下侵蚀和地貌危害形式之一。本研究使用了两个深度学习(DL)模型,包括混合 CNN-RNN 和与两个密集模型合并的集合 DL(EDL)。控制 LS 的主要变量(包括环境、水文、水文地质、数字高程模型和土壤特性)被用作预测性 DL 模型的输入。同样,为了确定每个参数的性能程度,还设立了不同的控制点。然后,我们使用接收器运行特征曲线下面积(ROC-AUC)和精度-召回图来训练和测试我们的 DL 模型。我们还采用了基于博弈论的测量方法,包括置换特征重要性测量(PFIM)和SHAPLEY Additive exPlanations(SHAP),以评估特征的相对重要性和预测模型输出的可解释性。我们的研究结果表明,在检测和绘制土地沉降图方面,CNN-RNN 模型的 ROC-AUC 曲线(0.95)为训练数据集的 1 类(土地沉降),而 EDL 的 ROC 曲线(0.93)为训练数据集的 1 类(土地沉降)。在检测和绘制土地沉降图的测试数据中,CNN-RNN 的第 1 类精度-召回曲线(0.954)与 EDL 模型的第 1 类精度-召回曲线(0.95)相比也表现良好。研究结果表明,研究区域的大部分地区容易发生地面沉降。模型敏感性分析结果表明,地下水下降率对模型最为敏感。根据 SHAP 值,地下水下降率被确定为对模型输出贡献最大的特征。这项研究的重要性体现在更广泛的层面上,尤其是在具有类似地貌和气候条件的干旱和半干旱环境中。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Detection of land subsidence using hybrid and ensemble deep learning models

Detection of land subsidence using hybrid and ensemble deep learning models

Land subsidence (LS) is among the most prominent forms of subsurface erosion and geomorphological hazards. This study used two deep learning (DL) models consisting of the hybrid CNN-RNN and ensemble DL (EDL) merged with two dense models. The main variables controlling LS (consisting of environmental, hydrological, hydrogeological, digital elevation model, and soil characteristics), were used as the input for the predictive DL models. Likewise, to establish the degree of performance of each parameter, different control points have been established. We then trained and tested our DL models using the receiver-operating characteristic-area under curve (ROC-AUC) and precision-recall plots. The measures based on the game theory consisting of permutation feature importance measure (PFIM) and SHapley Additive exPlanations (SHAP) were employed to assess the features relative importance and interpretability of the predictive model output. Our findings show that the ensemble CNN-RNN model performed well with the ROC-AUC curve (0.95) of class 1 (land subsidence) for training data for detecting and mapping land subsidence compared to EDL with the ROC curve (0.93) of class 1 (land subsidence) for training datasets. The CNN-RNN also performed well with the precision-recall curve (0.954) of class 1 for testing data for detecting and mapping land subsidence compared to the EDL model with the precision-recall curve (0.95) of class 1. The results of this research revealed that much of the study area is susceptible to land subsidence. The results of the model sensitivity analysis suggested that the groundwater drop rate is the most sensitive for the model. Based on the SHAP values, the groundwater drop rate was identified as the most contributed feature to the model output. The importance of this study is at a broader level, especially in arid and semiarid environments with similar geomorphological, and climatic conditions.

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来源期刊
Applied Geomatics
Applied Geomatics REMOTE SENSING-
CiteScore
5.40
自引率
3.70%
发文量
61
期刊介绍: Applied Geomatics (AGMJ) is the official journal of SIFET the Italian Society of Photogrammetry and Topography and covers all aspects and information on scientific and technical advances in the geomatics sciences. The Journal publishes innovative contributions in geomatics applications ranging from the integration of instruments, methodologies and technologies and their use in the environmental sciences, engineering and other natural sciences. The areas of interest include many research fields such as: remote sensing, close range and videometric photogrammetry, image analysis, digital mapping, land and geographic information systems, geographic information science, integrated geodesy, spatial data analysis, heritage recording; network adjustment and numerical processes. Furthermore, Applied Geomatics is open to articles from all areas of deformation measurements and analysis, structural engineering, mechanical engineering and all trends in earth and planetary survey science and space technology. The Journal also contains notices of conferences and international workshops, industry news, and information on new products. It provides a useful forum for professional and academic scientists involved in geomatics science and technology. Information on Open Research Funding and Support may be found here: https://www.springernature.com/gp/open-research/institutional-agreements
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