.png){kind=icon}
返回
Flood Susceptibility Modeling Using an Advanced Deep Learning-Based Iterative Classifier Optimizer
.png){kind=icon}
OA
AI
DOI：10.3390/land12040810.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
We developed a novel iterative classifier optimizer (ICO) with alternating decision tree (ADT), naive Bayes (NB), artificial neural network (ANN), and deep learning neural network (DLNN) ensemble algorithms to build novel ensemble computational models (ADT-ICO, NB-ICO, ANN-ICO, and DLNN-ICO) for flood susceptibility (FS) mapping in the Padma River basin, Bangladesh. The models consist of environmental, topographical, hydrological, and tectonic circumstances, and the final result was chosen based on the causative attributes using multicollinearity analysis. Statistical techniques were utilized to assess the model's performance. The results revealed that rainfall, elevation, and distance from the river are the most influencing variables for the occurrence of floods in the basin. The ensemble model of DLNN-ICO has optimal predictive performance (AUC = 0.93, and 0.91, sensitivity = 0.93 and 0.92, specificity = 0.90 and 0.80, F score = 0.91 and 0086 in the training and validation stages, respectively) followed by ADT-ICO, NB-ICO, and ANN-ICO, and might be a viable technique for precisely predicting and visualizing flood events.
Keyword:
flood risk
ADT
northwest Bangladesh
naive Bayes
Padma River basin
期刊
IF:
3.2
论文数:
1.3W
被引数:
2.5W
机构
引用论文
分享收藏CONTROL OF COMMUNICABLE DISEASES BY AGE-SPECIFIC IMMUNISATION SCHEDULES通过年龄特异性免疫接种计划控制传染病.png)
The Lancet
The LancetIF0
PREAI
分享收藏Prediction Success of Machine Learning Methods for Flash Flood Susceptibility Mapping in the Tafresh Watershed, Iran伊朗塔弗里希流域山洪敏感性制图的机器学习方法的预测成功SUSTAINABILITY
OAAI
SUSTAINABILITYIF3.3
OAAI分享收藏The Role of Hydrological Initial Conditions on Atmospheric River Floods in the Russian River Basin水文初始条件对俄罗斯流域大气河流洪水的作用OAAI
OAAI分享收藏Spatial prediction of flood-susceptible areas using frequency ratio and maximum entropy models基于频率比和最大熵模型的洪水易发区空间预测
PREAI
分享收藏Comparison of machine learning models for gully erosion susceptibility mapping沟壑侵蚀敏感性制图的机器学习模型比较GEOSCIENCE FRONTIERSOAAI
GEOSCIENCE FRONTIERSIF8.9
OAAI分享收藏Exploring effectiveness of frequency ratio and support vector machine models in storm surge flood susceptibility assessment: A study of Sundarban Biosphere Reserve, India探索频率比和支持向量机模型在风暴潮洪水敏感性评估中的有效性: 印度Sundarban生物圈保护区的研究CATENA
CATENAIF5.7
PREAI
分享收藏Application of stacking hybrid machine learning algorithms in delineating multi-type flooding in Bangladeshstacking混合机器学习算法在孟加拉国多类型洪水划分中的应用
PREAI
分享收藏
查看更多内容