Cited by Automatic flood detection using sentinel-1 images on the google earth engine

Flood susceptibility mapping through geoinformatics and ensemble learning methods, with an emphasis on the AdaBoost-Decision Tree algorithm, in Mazandaran, Iran DOI

Maryam Jahanbani,

Mohammad H. Vahidnia, Hossein Aghamohammadi

et al.

Earth Science Informatics, Journal Year: 2024, Volume and Issue: 17(2), P. 1433 - 1457

Published: Jan. 15, 2024

Language: Английский

Citations

Flood susceptibility mapping using an improved analytic network process with statistical models DOI

Peyman Yariyan, Mohammadtaghi Avand, Rahim Ali Abbaspour

et al.

Geomatics Natural Hazards and Risk, Journal Year: 2020, Volume and Issue: 11(1), P. 2282 - 2314

Published: Jan. 1, 2020

Flooding is a natural disaster that causes considerable damage to different sectors and severely affects economic social activities. The city of Saqqez in Iran susceptible flooding due its specific environmental characteristics. Therefore, susceptibility vulnerability mapping are essential for comprehensive management reduce the harmful effects flooding. primary purpose this study combine Analytic Network Process (ANP) decision-making method statistical models Frequency Ratio (FR), Evidential Belief Function (EBF), Ordered Weight Average (OWA) flood City Kurdistan Province, Iran. frequency ratio was used instead expert opinions weight criteria ANP. ten factors influencing area slope, rainfall, slope length, topographic wetness index, aspect, altitude, curvature, distance from river, geology, land use/land cover. We identified 42 points area, 70% which modelling, remaining 30% validate models. Receiver Operating Characteristic (ROC) curve evaluate results. under obtained ROC indicates superior performance ANP EBF hybrid model (ANP-EBF) with 95.1% efficiency compared combination FR (ANP-FR) 91% OWA (ANP-OWA) 89.6% efficiency.

Language: Английский

Citations

126

Ensemble models of GLM, FDA, MARS, and RF for flood and erosion susceptibility mapping: a priority assessment of sub-basins DOI

Amirhosein Mosavi,

Mohammad Golshan, Saeid Janizadeh

et al.

Geocarto International, Journal Year: 2020, Volume and Issue: 37(9), P. 2541 - 2560

Published: Sept. 28, 2020

The mountainous watersheds are increasingly challenged with extreme erosions and devastating floods due to climate change human interventions. Hazard mapping is essential for local policymaking prevention, planning the mitigation actions, also adaptation extremes. This study proposes novel predictive models susceptibility flood erosion. Furthermore, this elaborates on prioritizing existing sub-basins in terms of erosion susceptibility. A comparative analysis generalized linear model (GLM), flexible discriminate analyses (FDA), multivariate adaptive regression spline (MARS), random forest (RF), their ensemble performed ensure highest performance. priority sensitivity was determined based best model. results showed that GLM, FDA, MARS, RF, had an area under curve (AUC) 0.91, 0.92, 0.89, 0.93 0.94, respectively, modeling Also, AUC 0.93, 0.96, 0.97, determining Priority assessment model, approach, indicated SW3 SW5 were found have high soil erosion, respectively.

Language: Английский

Citations

113

Flood susceptibility mapping and assessment using a novel deep learning model combining multilayer perceptron and autoencoder neural networks DOI

Mohammad Ahmadlou, A’kif Al-Fugara, Abdel Rahman Al‐Shabeeb

et al.

Journal of Flood Risk Management, Journal Year: 2020, Volume and Issue: 14(1)

Published: Dec. 18, 2020

Abstract Floods are one of the most destructive natural disasters causing financial damages and casualties every year worldwide. Recently, combination data‐driven techniques with remote sensing (RS) geographical information systems (GIS) has been widely used by researchers for flood susceptibility mapping. This study presents a novel hybrid model combining multilayer perceptron (MLP) autoencoder models to produce maps two areas located in Iran India. For cases, nine, twelve factors were considered as predictor variables mapping, respectively. The prediction capability proposed was compared that traditional MLP through area under receiver operating characteristic (AUROC) criterion. AUROC curve autoencoder‐MLP were, respectively, 75 90, 74 93% training phase 60 91, 81 97% testing phase, India results suggested outperformed and, therefore, can be powerful other studies

Language: Английский

Citations

113

Estimation of axial load-carrying capacity of concrete-filled steel tubes using surrogate models DOI

Haï-Bang Ly, Binh Thai Pham, Lei Lü

et al.

Neural Computing and Applications, Journal Year: 2020, Volume and Issue: 33(8), P. 3437 - 3458

Published: July 25, 2020

Language: Английский

Citations

106

Modeling the fluctuations of groundwater level by employing ensemble deep learning techniques DOI

Haitham Abdulmohsin Afan,

Ahmedbahaaaldin Ibrahem Ahmed Osman,

Yusuf Essam

et al.

Engineering Applications of Computational Fluid Mechanics, Journal Year: 2021, Volume and Issue: 15(1), P. 1420 - 1439

Published: Jan. 1, 2021

This study proposes two techniques: Deep Learning (DL) and Ensemble (EDL) to predict groundwater level (GWL) for five wells in Malaysia. Two scenarios were proposed, scenario-1 (S1): GWL from 4 was used as inputs the fifth well scenario-2 (S2): time series with lag up 20 days all wells. The results S1 prove that ensemble EDL generally performs superior DL estimation of each station using data remaining four except Paya Indah Wetland which method provide better estimates compared EDL. Regarding S2, also exhibits performance predicting daily stations model. Implementing decreased RMSE, NAE RRMSE by 11.6%, 27.3% 22.3% increased R, Spearman rho Kendall tau 0.4%, 1.1% 3.5%, respectively. Moreover, S2 shows a high precision within less lag, ranging between 2 DL. Therefore, model has potential managing sustainability

Language: Английский

Citations

104

Examining LightGBM and CatBoost models for wadi flash flood susceptibility prediction DOI

Mohamed Saber, Tayeb Boulmaiz, Mawloud Guermoui

et al.

Geocarto International, Journal Year: 2021, Volume and Issue: 37(25), P. 7462 - 7487

Published: Aug. 31, 2021

This study presents two machine learning models, namely, the light gradient boosting (LightGBM) and categorical (CatBoost), for first time predicting flash flood susceptibility (FFS) in Wadi System (Hurghada, Egypt). A inventory map with 445 sites was produced randomly divided into groups training (70%) testing (30%). Fourteen controlling factors were selected evaluated their relative importance occurrence prediction. The performance of models assessed using various indexes comparison to common random forest (RF) method. results show areas under receiver operating characteristic curves (AUROC) above 97% all that LightGBM outperforms other terms classification metrics processing time. developed FFS maps demonstrate highly populated are most susceptible floods. present proves employed algorithms (LightGBM CatBoost) can be efficiently used mapping.

Language: Английский

Citations

102

Modeling groundwater potential using novel GIS-based machine-learning ensemble techniques DOI

Alireza Arabameri, Subodh Chandra Pal, Fatemeh Rezaie

et al.

Journal of Hydrology Regional Studies, Journal Year: 2021, Volume and Issue: 36, P. 100848 - 100848

Published: June 26, 2021

The present study has been carried out in the Tabriz River basin (5397 km2) north-western Iran. Elevations vary from 1274 to 3678 m above sea level, and slope angles range 0 150.9 %. average annual minimum maximum temperatures are 2 °C 12 °C, respectively. rainfall ranges 243 641 mm, northern southern parts of receive highest amounts. In this study, we mapped groundwater potential (GWP) with a new hybrid model combining random subspace (RS) multilayer perception (MLP), naïve Bayes tree (NBTree), classification regression (CART) algorithms. A total 205 spring locations were collected by integrating field surveys data Iran Water Resources Management, divided into 70:30 for training validation. Fourteen conditioning factors (GWCFs) used as independent inputs. Statistics such receiver operating characteristic (ROC) five others evaluate performance models. results show that all models performed well GWP mapping (AUC > 0.8). MLP-RS achieved high validation scores = 0.935). relative importance GWCFs was revealed slope, elevation, TRI HAND most important predictors presence. This demonstrates ensemble can support sustainable management resources.

Language: Английский

Citations

Comparison of gradient boosted decision trees and random forest for groundwater potential mapping in Dholpur (Rajasthan), India DOI