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电子工程代写|数据管理和数据系统代写Data Management and Data Systems代考|Landslide Conditioning Factor Selection Results

The prediction capability of collected conditioning factors was evaluated using a tree-based feature importance method. As per the results shown in Figs. 6 and 7 , rainfall has the highest prediction capability among all conditioning factors in both study areas. This result validates a previous research in the literature about rainfall being a primary factor which causes landslides [15] compared to other conditioning factors which have less impact compared to rainfall. In comparison to Ireland, aspect and curvature score in Ratnapura have very small average importance values.
If any conditioning factor scores a negligible value for Average Importance (AI), that factor needs to be removed [22] from the feature set. Since the curvature and aspect have shown a very low importance in Ratnapura area, they were not utilized in building the susceptibility map for Ratnapura. Also, the rest of the factors were selected to train the models since they all got significant AI score. This observation proves the hypothesis from previous research that the impact on these conditioning factors can vary based on the geological location [23]. Hence, these average feature importance values are most likely to change for a new landslide zone with different geological properties. Thus, it is highly recommended that the susceptibility map building researchers use the proposed feature importance calculating technique to quantify the importance of factors for each zone afresh.

In landslide modeling, it is essential to evaluate and assess the quality and productivity of the trained models. $F$-score, precision, and recall measurements scored by the trained models for both training dataset and test dataset are included in Tables 2 and 3 for Ratnapura and Ireland, respectively. All three models exhibit reasonably good predictive capability. For the test set, the highest F-score and precision values were scored by the random forest classifier. XGBoost produced higher recall value compared to the random forest and rotation forest classifiers. These observations can be seen in both study areas. Cross-validation results justify the fact that random forest and XGBoost models are not overfitted. However, rotation forest model can be considered as overfitted since it scores low performance on the test set relative to its train set.

电子工程代写|数据管理和数据系统代写Data Management and Data Systems代考|Model Validation

In machine learning-based modeling, one of the most critical phases is the validation phase of the prediction model. Validation help in two ways, it quantifies the ability of the model to work well with unseen examples and it also quantifies how accurately the model can perform for both seen and unseen examples. In this research, all the models were tested thoroughly to verify that the model is properly fitted to the training dataset without overfitting or underfitting.

Models can be assessed by referring to the known historical landslides data and comparing with the model predictions. A common approach is to split the dataset into two subsets labeled as a training and testing dataset with 80-20 split. 80\% portion will be used as training set, while the other unseen portion is used for testing. Since a landslide dataset is imbalanced, SMOTE sampling method was applied before the model training process in this study. This technique increases the number of samples for the minor class by interpolation.

However, the problem of overfitting in machine learning can still reside in the trained model which leads to being less precise on unseen data. The tenfold crossvalidation was conducted to make sure that the model is not overfitted. Data is divided into ten subsets such that each time, one of the subsets is used as a test set while other nine subsets are put together to form the training set.

In this study, landslide mapping was treated as a binary classification which produces two outputs as either as a landslide occurrence or a non-landslide occurrence.
Four possible prediction types are shown in the confusion matrix in Fig. $5 .$
TP (True Positive) and TN (True Negative) are the numbers of landslide cells that are correctly classified, and FP (False Positive) and FN (False Negative) are the numbers of landslide cells incorrectly classified.

电子工程代写|数据管理和数据系统代写Data Management and Data Systems代考|CMSC724

电子工程代写|数据管理和数据系统代写Data Management and Data Systems代考|Landslide Conditioning Factor Selection Results

使用基于树的特征重要性方法评估收集的条件因子的预测能力。根据图 1-2 所示的结果。如图 6 和 7 所示,降雨在两个研究区的所有调节因子中具有最高的预测能力。这一结果验证了文献中先前关于降雨是导致滑坡的主要因素 [15] 的研究,而其他调节因素与降雨相比影响较小。与爱尔兰相比,Ratnapura 的纵横比和曲率得分的平均重要性值非常小。
如果任何条件因子的平均重要性 (AI) 得分可以忽略不计,则需要从特征集中删除该因子 [22]。由于曲率和坡向在 Ratnapura 地区的重要性非常低,因此在构建 Ratnapura 的敏感性地图时没有使用它们。此外,选择其余因素来训练模型,因为它们都获得了显着的 AI 分数。这一观察结果证明了先前研究的假设,即对这些条件因素的影响可能因地理位置而异[23]。因此,对于具有不同地质性质的新滑坡带,这些平均特征重要性值最有可能发生变化。因此,

在滑坡建模中,评估和评估训练模型的质量和生产力至关重要。F- 训练模型对训练数据集和测试数据集的得分、精度和召回率测量值分别包含在 Ratnapura 和爱尔兰的表 2 和表 3 中。这三个模型都表现出相当好的预测能力。对于测试集,最高 F 分数和精度值由随机森林分类器评分。与随机森林和旋转森林分类器相比,XGBoost 产生了更高的召回值。在这两个研究领域都可以看到这些观察结果。交叉验证结果证明了随机森林和 XGBoost 模型没有过度拟合的事实。然而,旋转森林模型可以被认为是过拟合的,因为它在测试集上相对于它的训练集得分较低。

电子工程代写|数据管理和数据系统代写Data Management and Data Systems代考|Model Validation

在基于机器学习的建模中,最关键的阶段之一是预测模型的验证阶段。验证有两个方面的帮助,它量化了模型处理未见示例的能力,还量化了模型对已见和未见示例执行的准确度。在这项研究中,对所有模型进行了彻底的测试,以验证模型是否正确地拟合到训练数据集,没有过拟合或欠拟合。

模型可以通过参考已知的历史滑坡数据并与模型预测进行比较来评估。一种常见的方法是将数据集拆分为两个子集,分别标记为训练和测试数据集,拆分为 80-20。80% 的部分将用作训练集,而其他看不见的部分将用于测试。由于滑坡数据集不平衡,本研究在模型训练过程之前应用了 SMOTE 采样方法。这种技术通过插值增加了次要类的样本数量。

然而,机器学习中的过度拟合问题仍然存在于训练后的模型中,这会导致对未见数据的精确度降低。进行了十倍交叉验证以确保模型没有过度拟合。将数据分成十个子集,每次将其中一个子集用作测试集,而将其他九个子集放在一起形成训练集。

在这项研究中,滑坡测绘被视为一种二元分类,它产生两个输出作为滑坡发生或非滑坡发生。
图 1 中的混淆矩阵显示了四种可能的预测类型。5.
TP(True Positive)和TN(True Negative)是正确分类的滑坡单元数,FP(False Positive)和FN(False Negative)是错误分类的滑坡单元数。

电子工程代写|数据管理和数据系统代写Data Management and Data Systems代考

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