Flood prediction using machine learning models: Literature review
TLDR
In this paper, the state-of-the-art machine learning models for both long-term and short-term floods are evaluated and compared using a qualitative analysis of robustness, accuracy, effectiveness and speed.Abstract:
Floods are among the most destructive natural disasters, which are highly complex to model. The research on the advancement of flood prediction models contributed to risk reduction, policy suggestion, minimization of the loss of human life, and reduction of the property damage associated with floods. To mimic the complex mathematical expressions of physical processes of floods, during the past two decades, machine learning (ML) methods contributed highly in the advancement of prediction systems providing better performance and cost-effective solutions. Due to the vast benefits and potential of ML, its popularity dramatically increased among hydrologists. Researchers through introducing novel ML methods and hybridizing of the existing ones aim at discovering more accurate and efficient prediction models. The main contribution of this paper is to demonstrate the state of the art of ML models in flood prediction and to give insight into the most suitable models. In this paper, the literature where ML models were benchmarked through a qualitative analysis of robustness, accuracy, effectiveness, and speed are particularly investigated to provide an extensive overview on the various ML algorithms used in the field. The performance comparison of ML models presents an in-depth understanding of the different techniques within the framework of a comprehensive evaluation and discussion. As a result, this paper introduces the most promising prediction methods for both long-term and short-term floods. Furthermore, the major trends in improving the quality of the flood prediction models are investigated. Among them, hybridization, data decomposition, algorithm ensemble, and model optimization are reported as the most effective strategies for the improvement of ML methods. This survey can be used as a guideline for hydrologists as well as climate scientists in choosing the proper ML method according to the prediction task.read more
Citations
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Journal ArticleDOI
A Rainfall-Runoff Model With LSTM-Based Sequence-to-Sequence Learning
TL;DR: The LSTM‐seq2seq model shows sufficient predictive power and could be used to improve forecast accuracy in short‐term flood forecast applications and the seq2seq method was demonstrated to be an effective method for time series predictions in hydrology.
Journal ArticleDOI
Short-term runoff prediction with GRU and LSTM networks without requiring time step optimization during sample generation
Shuai Gao,Yuefei Huang,Yuefei Huang,Shuo Zhang,Jing-Cheng Han,Guangqian Wang,Meixin Zhang,Qingsheng Lin +7 more
TL;DR: Two popular variants of Recurrent Neural Network named Long Short-Term Memory and Gated Recurrent Unit networks were employed to develop new data-driven flood forecasting models, showing that GRU models perform equally well as LSTM models and GRU may be the preferred method in short term runoff predictions.
Journal ArticleDOI
A review of machine learning applications in wildfire science and management
Piyush Jain,Piyush Jain,Sean C. P. Coogan,Sriram Subramanian,Mark Crowley,Steve W. Taylor,Mike D. Flannigan +6 more
TL;DR: Artificial intelligence has been applied in wildfire science and management since the 1990s, with early applications including neural networks and expert systems as discussed by the authors, and it has rapidly accelerated the field's development.
Journal ArticleDOI
Flash-flood hazard assessment using ensembles and Bayesian-based machine learning models: Application of the simulated annealing feature selection method.
Farzaneh Sajedi Hosseini,Bahram Choubin,Amir Mosavi,Narjes Nabipour,Shahaboddin Shamshirband,Hamid Darabi,Ali Torabi Haghighi +6 more
TL;DR: The current research proposes the state-of-the-art ensemble models of boosted generalized linear model (GLMBoost) and random forest (RF) and Bayesian generalizedlinear model (BayesGLM) methods for higher performance modeling and a pre-processing method is used to eliminate redundant variables from the modeling process.
Journal ArticleDOI
Process-Guided Deep Learning Predictions of Lake Water Temperature
Jordan S. Read,Xiaowei Jia,Jared Willard,Alison P. Appling,Jacob A. Zwart,Samantha K. Oliver,Anuj Karpatne,Gretchen J. A. Hansen,Paul C. Hanson,William Watkins,Michael Steinbach,Vipin Kumar +11 more
TL;DR: In this paper, the authors presented the results of the North Central Climate Adaptation Science Center (NCAACS) at the University of Minnesota (U.M. System) with the help of the National Science Foundation (NSF).
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