Flood susceptibility mapping using meta-heuristic algorithms DOI Creative Commons
Alireza Arabameri, Amir Seyed Danesh,

M. Santosh

et al.

Geomatics Natural Hazards and Risk, Journal Year: 2022, Volume and Issue: 13(1), P. 949 - 974

Published: April 11, 2022

Flood is a common global natural hazard, and detailed flood susceptibility maps for specific watersheds are important management measures. We compute the map Kaiser watershed in Iran using machine learning models such as support vector (SVM), Particle swarm optimization (PSO), genetic algorithm (GA) along with ensembles (PSO-GA SVM-GA). The application of assessment mapping analyzed, future research suggestions presented. model was constructed based on fifteen causatives: slope, slope aspect, elevation, plan curvature, land use, cover, normalize differences vegetation index (NDVI), convergence (CI), topographical wetness (TWI), topographic positioning Index (TPI), drainage density (DD), distance to stream, terrain ruggedness (TRI), surface texture (TST), geology stream power (SPI) inventory data which later divided by 70% training 30% validated model. output evaluated through sensitivity, specificity, accuracy, precision, Cohen Kappa, F-score, receiver operating curve (ROC). evaluation method shows robust results from (0.839), particle (0.851), (0.874), SVM-GA (0.886), PSO-GA (0.902). Compared have done some methods commonly used this assessment. A high-quality, informative database essential classification types that very helpful improve performances. performance ensemble better than model, yielding high degree accuracy (AUC-0.902%). Our approach, therefore, provides novel studies other watersheds.

Language: Английский

Machine learning in modelling land-use and land cover-change (LULCC): Current status, challenges and prospects DOI
Junye Wang,

Michael Bretz,

M. Ali Akber Dewan

et al.

The Science of The Total Environment, Journal Year: 2022, Volume and Issue: 822, P. 153559 - 153559

Published: Jan. 31, 2022

Language: Английский

Citations

244
Predicting flood susceptibility using LSTM neural networks DOI Creative Commons
Zhice Fang, Yi Wang, Ling Peng

et al.

Journal of Hydrology, Journal Year: 2020, Volume and Issue: 594, P. 125734 - 125734

Published: Nov. 8, 2020

Identifying floods and producing flood susceptibility maps are crucial steps for decision-makers to prevent manage disasters. Plenty of studies have used machine learning models produce reliable maps. Nevertheless, most research ignores the importance developing appropriate feature engineering methods. In this study, we propose a local spatial sequential long short-term memory neural network (LSS-LSTM) prediction in Shangyou County, China. The three main contributions study summarized below. First all, it is new perspective use deep technique LSTM prediction. Second, integrate an method with predict susceptibility. Third, implement two optimization techniques data augmentation batch normalization further improve performance proposed method. LSS-LSTM can not only capture attribution information conditioning factors data, but also has powerful modelling capabilities deal relationship floods. experimental results demonstrate that achieves satisfactory (93.75% 0.965) terms accuracy area under receiver operating characteristic (ROC) curve.

Language: Английский

Citations

208
Flash Flood Susceptibility Modeling Using New Approaches of Hybrid and Ensemble Tree-Based Machine Learning Algorithms DOI Creative Commons
Shahab S. Band, Saeid Janizadeh, Subodh Chandra Pal

et al.

Remote Sensing, Journal Year: 2020, Volume and Issue: 12(21), P. 3568 - 3568

Published: Oct. 31, 2020

Flash flooding is considered one of the most dynamic natural disasters for which measures need to be taken minimize economic damages, adverse effects, and consequences by mapping flood susceptibility. Identifying areas prone flash a crucial step in hazard management. In present study, Kalvan watershed Markazi Province, Iran, was chosen evaluate susceptibility modeling. Thus, detect flood-prone zones this study area, five machine learning (ML) algorithms were tested. These included boosted regression tree (BRT), random forest (RF), parallel (PRF), regularized (RRF), extremely randomized trees (ERT). Fifteen climatic geo-environmental variables used as inputs models. The results showed that ERT optimal model with an area under curve (AUC) value 0.82. rest models’ AUC values, i.e., RRF, PRF, RF, BRT, 0.80, 0.79, 0.78, 0.75, respectively. model, areal coverage very high moderate susceptible 582.56 km2 (28.33%), portion associated low zones. It concluded topographical hydrological parameters, e.g., altitude, slope, rainfall, river’s distance, effective parameters. will play vital role planning implementation mitigation strategies region.

Language: Английский

Citations

196
Flooding and its relationship with land cover change, population growth, and road density DOI Creative Commons

Mahfuzur Rahman,

Chen Ningsheng,

Golam Iftekhar Mahmud

et al.

Geoscience Frontiers, Journal Year: 2021, Volume and Issue: 12(6), P. 101224 - 101224

Published: May 5, 2021

Bangladesh experiences frequent hydro-climatic disasters such as flooding. These are believed to be associated with land use changes and climate variability. However, identifying the factors that lead flooding is challenging. This study mapped flood susceptibility in northeast region of using Bayesian regularization back propagation (BRBP) neural network, classification regression trees (CART), a statistical model (STM) evidence belief function (EBF), their ensemble models (EMs) for three time periods (2000, 2014, 2017). The accuracy machine learning algorithms (MLAs), STM, EMs were assessed by considering area under curve—receiver operating characteristic (AUC-ROC). Evaluation levels aforementioned revealed EM4 (BRBP-CART-EBF) outperformed (AUC > 90%) standalone other analyzed. Furthermore, this investigated relationships among cover change (LCC), population growth (PG), road density (RD), relative (RCF) areas period between 2000 2017. results showed very high increased 19.72% 2017, while PG rate 51.68% over same period. Pearson correlation coefficient RCF RD was calculated 0.496. findings highlight significant association floods causative factors. could valuable policymakers resource managers they can improvements management reduction damage risks.

Language: Английский

Citations

146
Flood Susceptibility Assessment Using Novel Ensemble of Hyperpipes and Support Vector Regression Algorithms DOI Open Access
Asish Saha, Subodh Chandra Pal, Alireza Arabameri

et al.

Water, Journal Year: 2021, Volume and Issue: 13(2), P. 241 - 241

Published: Jan. 19, 2021

Recurrent floods are one of the major global threats among people, particularly in developing countries like India, as this nation has a tropical monsoon type climate. Therefore, flood susceptibility (FS) mapping is indeed necessary to overcome natural hazard phenomena. With mind, we evaluated prediction performance FS Koiya River basin, Eastern India. The present research work was done through preparation sophisticated inventory map; eight conditioning variables were selected based on topography and hydro-climatological condition, by applying novel ensemble approach hyperpipes (HP) support vector regression (SVR) machine learning (ML) algorithms. HP-SVR also compared with stand-alone ML algorithms HP SVR. In relative importance variables, distance river most dominant factor for occurrences followed rainfall, land use cover (LULC), normalized difference vegetation index (NDVI). validation accuracy assessment maps five popular statistical methods. result evaluation showed that optimal model (AUC = 0.915, sensitivity 0.932, specificity 0.902, 0.928 Kappa 0.835) assessment, 0.885) SVR 0.871).

Language: Английский

Citations

142
Towards better flood risk management: Assessing flood risk and investigating the potential mechanism based on machine learning models DOI
Jialei Chen, Guoru Huang, Wenjie Chen

et al.

Journal of Environmental Management, Journal Year: 2021, Volume and Issue: 293, P. 112810 - 112810

Published: May 21, 2021

Language: Английский

Citations

135
Evaluation of different boosting ensemble machine learning models and novel deep learning and boosting framework for head-cut gully erosion susceptibility DOI
Wei Chen, Xinxiang Lei, Rabin Chakrabortty

et al.

Journal of Environmental Management, Journal Year: 2021, Volume and Issue: 284, P. 112015 - 112015

Published: Jan. 27, 2021

Language: Английский

Citations

131
Mapping the spatial and temporal variability of flood hazard affected by climate and land-use changes in the future DOI Creative Commons
Saeid Janizadeh, Subodh Chandra Pal, Asish Saha

et al.

Journal of Environmental Management, Journal Year: 2021, Volume and Issue: 298, P. 113551 - 113551

Published: Aug. 17, 2021

The predicts current and future flood risk in the Kalvan watershed of northwestern Markazi Province, Iran. To do this, 512 non-flood locations were identified mapped. Twenty flood-risk factors selected to model using several machine learning techniques: conditional inference random forest (CIRF), gradient boosting (GBM), extreme (XGB) their ensembles. investigate (year 2050) effects changing climates land use on risk, a general circulation (GCM) with representative concentration pathways (RCPs) 2.6 8.5 scenarios by 2050 was tested for impacts 8 precipitation variables. In addition, uses prepared CA-Markov model. performances models validated Receiver Operating Characteristic-Area Under Curve (ROC-AUC) other statistical analyses. AUC value ROC curve indicates that ensemble had highest predictive power (AUC = 0.83) followed GBM 0.80), XGB 0.79), CIRF 0.78). results climate changes flood-prone areas showed classified as having moderate very high will increase 2050. Due occurring climates, area increased predictions from all four models. areal proportion classes zones under RCP scenario have changed following proportions distribution Very Low −12.04 %, −8.56 Moderate +1.56 High +11.55 +7.49 %. has caused present percentages: −14.48 −6.35 +4.54 +10.61 +5.67 mapping can aid planners hazard managers efforts mitigate impacts.

Language: Английский

Citations

130
Flood susceptibility mapping by integrating frequency ratio and index of entropy with multilayer perceptron and classification and regression tree DOI
Yi Wang, Zhice Fang, Haoyuan Hong

et al.

Journal of Environmental Management, Journal Year: 2021, Volume and Issue: 289, P. 112449 - 112449

Published: April 1, 2021

Language: Английский

Citations

122
A novel hybrid of meta-optimization approach for flash flood-susceptibility assessment in a monsoon-dominated watershed, Eastern India DOI

Dipankar Ruidas,

Rabin Chakrabortty, Abu Reza Md. Towfiqul Islam

et al.

Environmental Earth Sciences, Journal Year: 2022, Volume and Issue: 81(5)

Published: Feb. 21, 2022

Language: Английский

Citations

94