Flash flood susceptibility mapping using a novel deep learning model based on deep belief network, back propagation and genetic algorithm

The efficiency of deep learning and tree-based machine learning approaches has gained immense popularity in various fields. One deep learning model viz. convolution neural network (CNN), artificial neural network (ANN) and four tree-based machine learning models, namely, alternative decision tree (ADTree), classification and regression tree (CART), functional tree and logistic model tree (LMT), were used for landslide susceptibility mapping in the East Sikkim Himalaya region of India, and the results were compared. Landslide areas were delimited and mapped as landslide inventory (LIM) after gathering information from historical records and periodic field investigations. In LIM, 91 landslides were plotted and classified into training (64 landslides) and testing (27 landslides) subsets randomly to train and validate the models. A total of 21 landslide conditioning factors (LCFs) were considered as model inputs, and the results of each model were categorised under five susceptibility classes. The receiver operating characteristics curve and 21 statistical measures were used to evaluate and prioritise the models. The CNN deep learning model achieved the priority rank 1 with area under the curve of 0.918 and 0.933 by using the training and testing data, quantifying 23.02% and 14.40% area as very high and highly susceptible followed by ANN, ADtree, CART, FTree and LMT models. This research might be useful in landslide studies, especially in locations with comparable geophysical and climatological characteristics, to aid in decision making for land use planning.

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A Deep Learning Model for Predicting Movie Box Office Based on Deep Belief Network

Lecture Notes in Computer Science - Advances in Swarm Intelligence ◽

10.1007/978-3-319-93818-9_51 ◽

2018 ◽

pp. 530-541 ◽

Cited By ~ 1

Author(s):

Wei Wang ◽

Jiapeng Xiu ◽

Zhengqiu Yang ◽

Chen Liu

Keyword(s):

Deep Learning ◽

Learning Model ◽

Deep Belief Network ◽

Belief Network ◽

Box Office ◽

Deep Learning Model

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Prediction Success of Machine Learning Methods for Flash Flood Susceptibility Mapping in the Tafresh Watershed, Iran

Sustainability ◽

10.3390/su11195426 ◽

2019 ◽

Vol 11 (19) ◽

pp. 5426 ◽

Cited By ~ 47

Author(s):

Saeid Janizadeh ◽

Mohammadtaghi Avand ◽

Abolfazl Jaafari ◽

Tran Van Phong ◽

Mahmoud Bayat ◽

...

Keyword(s):

Machine Learning ◽

Flash Flood ◽

Susceptibility Mapping ◽

Annual Rainfall ◽

Slope Aspect ◽

Flood Events ◽

Learning Methods ◽

Machine Learning Methods ◽

Flood Susceptibility ◽

Flood Susceptibility Mapping

Floods are some of the most destructive and catastrophic disasters worldwide. Development of management plans needs a deep understanding of the likelihood and magnitude of future flood events. The purpose of this research was to estimate flash flood susceptibility in the Tafresh watershed, Iran, using five machine learning methods, i.e., alternating decision tree (ADT), functional tree (FT), kernel logistic regression (KLR), multilayer perceptron (MLP), and quadratic discriminant analysis (QDA). A geospatial database including 320 historical flood events was constructed and eight geo-environmental variables—elevation, slope, slope aspect, distance from rivers, average annual rainfall, land use, soil type, and lithology—were used as flood influencing factors. Based on a variety of performance metrics, it is revealed that the ADT method was dominant over the other methods. The FT method was ranked as the second-best method, followed by the KLR, MLP, and QDA. Given a few differences between the goodness-of-fit and prediction success of the methods, we concluded that all these five machine-learning-based models are applicable for flood susceptibility mapping in other areas to protect societies from devastating floods.

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