Flood hazards susceptibility mapping using statistical, fuzzy logic, and MCDM methods

Soft Computing, Год журнала: 2021, Номер 25(14), С. 9325 - 9346

Опубликована: Май 26, 2021

Язык: Английский

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Application of stacking hybrid machine learning algorithms in delineating multi-type flooding in Bangladesh

Journal of Environmental Management, Год журнала: 2021, Номер 295, С. 113086 - 113086

Опубликована: Июнь 18, 2021

Язык: Английский

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A comprehensive comparison of recent developed meta-heuristic algorithms for streamflow time series forecasting problem

Applied Soft Computing, Год журнала: 2021, Номер 105, С. 107282 - 107282

Опубликована: Март 18, 2021

Язык: Английский

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Landslide susceptibility mapping using artificial neural network tuned by metaheuristic algorithms

Environmental Earth Sciences, Год журнала: 2021, Номер 80(24)

Опубликована: Ноя. 27, 2021

Язык: Английский

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Hybrid Models Incorporating Bivariate Statistics and Machine Learning Methods for Flash Flood Susceptibility Assessment Based on Remote Sensing Datasets

Remote Sensing, Год журнала: 2021, Номер 13(23), С. 4945 - 4945

Опубликована: Дек. 5, 2021

Flash floods are considered to be one of the most destructive natural hazards, and they difficult accurately model predict. In this study, three hybrid models were proposed, evaluated, used for flood susceptibility prediction in Dadu River Basin. These integrate a bivariate statistical method fuzzy membership value (FMV) machine learning methods support vector (SVM), classification regression trees (CART), convolutional neural network (CNN). Firstly, geospatial database was prepared comprising nine conditioning factors, 485 locations, non-flood locations. Then, train test models. Subsequently, receiver operating characteristic (ROC) curve, seed cell area index (SCAI), accuracy evaluate performances The results reveal following: (1) ROC curve highlights fact that CNN-FMV had best fitting performance, under (AUC) values success rate 0.935 0.912, respectively. (2) Based on performance evaluation methods, all better capabilities than their respective single Compared with models, AUC SVM-FMV, CART-FMV, 0.032, 0.005, 0.055 higher; SCAI 0.05, 0.03, 0.02 lower; accuracies 4.48%, 1.38%, 5.86% higher, (3) indices, between 13.21% 22.03% study characterized by high very susceptibilities. proposed especially CNN-FMV, have potential application assessment specific areas future studies.

Язык: Английский

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Development of flood hazard map and emergency relief operation system using hydrodynamic modeling and machine learning algorithm

Journal of Cleaner Production, Год журнала: 2021, Номер 311, С. 127594 - 127594

Опубликована: Май 27, 2021

Язык: Английский

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Flood susceptibility modeling based on new hybrid intelligence model: Optimization of XGboost model using GA metaheuristic algorithm

Advances in Space Research, Год журнала: 2022, Номер 69(9), С. 3301 - 3318

Опубликована: Фев. 22, 2022

Язык: Английский

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Assessment of groundwater potential modeling using support vector machine optimization based on Bayesian multi-objective hyperparameter algorithm

Applied Soft Computing, Год журнала: 2022, Номер 132, С. 109848 - 109848

Опубликована: Ноя. 25, 2022

Язык: Английский

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Enhanced LSTM Model for Daily Runoff Prediction in the Upper Huai River Basin, China

Engineering, Год журнала: 2022, Номер 24, С. 229 - 238

Опубликована: Апрель 28, 2022

Runoff prediction is of great significance to flood defense. However, due the complexity and randomness runoff process, it hard predict daily accurately, especially for peak runoff. To address this issue, study proposes an enhanced long short-term memory (LSTM) model prediction, where novel loss functions are introduced feature extractors integrated. Two (peak error tanh (PET), swish (PES)) designed strengthen importance runoff's while weakening weight normal prediction. The extractor consisting three LSTM networks established each meteorological station, aiming extract temporal features input data at station. Taking upper Huai River Basin in China as a case study, from 1960–2016 predicted using model. Results indicate that performed well, achieving Nash–Sutcliffe efficiency (NSE) coefficient ranging 0.917–0.924 during validation period (November 2005–December 2016), outperforming widely used lumped hydrological models (Australian Water Balance Model (AWBM), Sacramento, SimHyd Tank Model) data-driven (artificial neural network (ANN), support vector regression (SVR), gated recurrent units (GRU)). with PES function best on extreme mean NSE floods 0.873. In addition, precipitation station higher altitude contributes more than closest stations. This provides effective tool which will benefit basin's defense water security management.

Язык: Английский

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Global Flood Mapper: a novel Google Earth Engine application for rapid flood mapping using Sentinel-1 SAR

Natural Hazards, Год журнала: 2022, Номер 114(2), С. 1341 - 1363

Опубликована: Июнь 21, 2022

Язык: Английский

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