Journal ArticleDOI
A novel deep learning neural network approach for predicting flash flood susceptibility: A case study at a high frequency tropical storm area.
Dieu Tien Bui,Nhat-Duc Hoang,Francisco Martínez-Álvarez,Phuong Thao Thi Ngo,Pham Viet Hoa,Tien Dat Pham,Pijush Samui,Romulus Costache +7 more
TLDR
It could be concluded that the proposed hybridization of GIS and deep learning can be a promising tool to assist the government authorities and involving parties in flash flood mitigation and land-use planning.About:
This article is published in Science of The Total Environment.The article was published on 2020-01-20. It has received 228 citations till now. The article focuses on the topics: Artificial neural network & Deep learning.read more
Citations
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Journal ArticleDOI
Flood susceptibility modelling using advanced ensemble machine learning models
Abu Reza Md. Towfiqul Islam,Swapan Talukdar,Susanta Mahato,Sonali Kundu,Kutub Uddin Eibek,Quoc Bao Pham,Alban Kuriqi,Nguyen Thi Thuy Linh +7 more
TL;DR: The methodology and solution-oriented results presented in this paper will assist the regional as well as local authorities and the policy-makers for mitigating the risks related to floods and also help in developing appropriate mitigation measures to avoid potential damages.
Journal ArticleDOI
A spatially explicit deep learning neural network model for the prediction of landslide susceptibility
Dong Van Dao,Abolfazl Jaafari,Mahmoud Bayat,Davood Mafi-Gholami,Chongchong Qi,Hossein Moayedi,Tran Van Phong,Hai-Bang Ly,Tien-Thinh Le,Phan Trong Trinh,Chinh Luu,Nguyen Kim Quoc,Bui Nhi Thanh,Binh Thai Pham +13 more
TL;DR: A comparative analysis using the Wilcoxon signed-rank tests revealed a significant improvement of landslide prediction using the spatially explicit DL model over the quadratic discriminant analysis, Fisher's linear discriminantAnalysis, and multi-layer perceptron neural network.
Journal ArticleDOI
A comprehensive review of deep learning applications in hydrology and water resources
TL;DR: This study provides a comprehensive review of state-of-the-art deep learning approaches used in the water industry for generation, prediction, enhancement, and classification tasks, and serves as a guide for how to utilize available deep learning methods for future water resources challenges.
Journal ArticleDOI
Short-term water quality variable prediction using a hybrid CNN–LSTM deep learning model
TL;DR: In this paper, a coupled CNN-LSTM model was proposed to predict water quality variables, namely dissolved oxygen (DO; mg/L) and chlorophyll-a (Chl-a; µg/L), in the Small Prespa Lake in Greece.
Journal ArticleDOI
Evaluation of deep learning algorithms for national scale landslide susceptibility mapping of Iran
Phuong Thao Thi Ngo,Mahdi Panahi,Khabat Khosravi,Omid Ghorbanzadeh,Narges Kariminejad,Artemi Cerdà,Saro Lee +6 more
TL;DR: Two novel deep learning algorithms, the recurrent neural network (RNN) and convolutional Neural Network (CNN), are applied for national-scale landslide susceptibility mapping of Iran to generate landslide susceptibility maps of Iran.
References
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Proceedings Article
Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
TL;DR: This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework.
Statistical learning theory
TL;DR: Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more.
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Practical statistics for medical research
TL;DR: Practical Statistics for Medical Research is a problem-based text for medical researchers, medical students, and others in the medical arena who need to use statistics but have no specialized mathematics background.
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Induction of Decision Trees
TL;DR: In this paper, an approach to synthesizing decision trees that has been used in a variety of systems, and it describes one such system, ID3, in detail, is described, and a reported shortcoming of the basic algorithm is discussed.