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Dr. Saro Lee
Korea Institutue of Geoscience and Mineral Resources

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0 GIS
0 Groundwater
0 Landslide
0 Machine Learning

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Journal article
Published: 18 July 2021 in Journal of Hydrology
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Identification of flood-prone sites in urban environments is necessary, but there is insufficient hydraulic information and time series data on surface runoff. To date, several attempts have been made to apply deep-learning models for flood hazard mapping in urban areas. This study evaluated the capability of convolutional neural network (NNETC) and recurrent neural network (NNETR) models for flood hazard mapping. A flood-inundation inventory (including 295 flooded sites) was used as the response variable and 10 flood-affecting factors were considered as the predictor variables. Flooded sites were then spatially randomly split in a 70:30 ratio for building flood models and for validation purposes. The prediction quality of the models was validated using the area under the receiver operating characteristic curve (AUC) and root mean square error (RMSE). The validation results indicated that prediction performance of the NNETC model (AUC = 84%, RMSE = 0.163) was slightly better than that of the NNETR model (AUC = 82%, RMSE = 0.186). Both models indicated that terrain ruggedness index was the most important predictor, followed by slope and elevation. Although the model output had a relative error of up to 20% (based on AUC), this modeling approach could still be used as a reliable and rapid tool to generate a flood hazard map for urban areas, provided that a flood inundation inventory is available.

ACS Style

Xinxiang Lei; Wei Chen; Mahdi Panahi; Fatemeh Falah; Omid Rahmati; Evelyn Uuemaa; Zahra Kalantari; Carla Sofia Santos Ferreira; Fatemeh Rezaie; John P. Tiefenbacher; Saro Lee; Huiyuan Bian. Urban flood modeling using deep-learning approaches in Seoul, South Korea. Journal of Hydrology 2021, 601, 126684 .

AMA Style

Xinxiang Lei, Wei Chen, Mahdi Panahi, Fatemeh Falah, Omid Rahmati, Evelyn Uuemaa, Zahra Kalantari, Carla Sofia Santos Ferreira, Fatemeh Rezaie, John P. Tiefenbacher, Saro Lee, Huiyuan Bian. Urban flood modeling using deep-learning approaches in Seoul, South Korea. Journal of Hydrology. 2021; 601 ():126684.

Chicago/Turabian Style

Xinxiang Lei; Wei Chen; Mahdi Panahi; Fatemeh Falah; Omid Rahmati; Evelyn Uuemaa; Zahra Kalantari; Carla Sofia Santos Ferreira; Fatemeh Rezaie; John P. Tiefenbacher; Saro Lee; Huiyuan Bian. 2021. "Urban flood modeling using deep-learning approaches in Seoul, South Korea." Journal of Hydrology 601, no. : 126684.

Journal article
Published: 12 July 2021 in Geocarto International
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Flood-susceptibility mapping is an important component of flood risk management to control the effects of natural hazards and prevention of injury. We used a remote-sensing and geographic information system (GIS) platform and a machine-learning model to develop a flood susceptibility map of Kangsabati River Basin, India where flash flood is common due to monsoon precipitation with short duration and high intensity. And in this subtropical region, climate change’s impact helps to influence the distribution of rainfall and temperature variation. We tested three models-particle swarm optimization (PSO), an artificial neural network (ANN), and a deep-leaning neural network (DLNN)-and prepared a final flood susceptibility map to classify flood-prone regions in the study area. Environmental, topographical, hydrological, and geological conditions were included in the models, and the final model was selected based on the relations between potentiality of causative factors and flood risk based on multi-collinearity analysis. The model results were validated and evaluated using the area under receiver operating characteristic (ROC) curve (AUC), which is an indicator of the current state of the environment and a value >0.95 implies a greater risk of flash floods. The AUC values for ANN, DLNN, and PSO for training datasets were 0.914, 0.920, and 0.942, respectively. Among these three models, PSO showed the best performance with an AUC value of 0.942. The PSO approach is applicable for flood susceptibility mapping of the eastern part of India, a subtropical region, to allow flood mitigation and help to improve risk management in this region.

ACS Style

Rabin Chakrabortty; Subodh Chandra Pal; Fatemeh Rezaie; Alireza Arabameri; Saro Lee; Paramita Roy; Asish Saha; Indrajit Chowdhuri; Hossein Moayedi. Flash-Flood Hazard Susceptibility Mapping in Kangsabati River Basin, India. Geocarto International 2021, 1 -21.

AMA Style

Rabin Chakrabortty, Subodh Chandra Pal, Fatemeh Rezaie, Alireza Arabameri, Saro Lee, Paramita Roy, Asish Saha, Indrajit Chowdhuri, Hossein Moayedi. Flash-Flood Hazard Susceptibility Mapping in Kangsabati River Basin, India. Geocarto International. 2021; ():1-21.

Chicago/Turabian Style

Rabin Chakrabortty; Subodh Chandra Pal; Fatemeh Rezaie; Alireza Arabameri; Saro Lee; Paramita Roy; Asish Saha; Indrajit Chowdhuri; Hossein Moayedi. 2021. "Flash-Flood Hazard Susceptibility Mapping in Kangsabati River Basin, India." Geocarto International , no. : 1-21.

Journal article
Published: 26 June 2021 in Journal of Hydrology: Regional Studies
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The present study has been carried out in the Tabriz River basin (5397 km2) in north-western Iran. Elevations vary from 1274 to 3678 m above sea level, and slope angles range from 0 to 150.9 %. The average annual minimum and maximum temperatures are 2 °C and 12 °C, respectively. The average annual rainfall ranges from 243 to 641 mm, and the northern and southern parts of the basin receive the highest amounts. In this study, we mapped the groundwater potential (GWP) with a new hybrid model combining random subspace (RS) with the multilayer perception (MLP), naïve Bayes tree (NBTree), and classification and regression tree (CART) algorithms. A total of 205 spring locations were collected by integrating field surveys with data from Iran Water Resources Management, and divided into 70:30 for training and validation. Fourteen groundwater conditioning factors (GWCFs) were used as independent model inputs. Statistics such as receiver operating characteristic (ROC) and five others were used to evaluate the performance of the models. The results show that all models performed well for GWP mapping (AUC > 0.8). The hybrid MLP-RS model achieved high validation scores (AUC = 0.935). The relative importance of GWCFs was revealed that slope, elevation, TRI and HAND are the most important predictors of groundwater presence. This study demonstrates that hybrid ensemble models can support sustainable management of groundwater resources.

ACS Style

Alireza Arabameri; Subodh Chandra Pal; Fatemeh Rezaie; Omid Asadi Nalivan; Indrajit Chowdhuri; Asish Saha; Saro Lee; Hossein Moayedi. Modeling groundwater potential using novel GIS-based machine-learning ensemble techniques. Journal of Hydrology: Regional Studies 2021, 36, 100848 .

AMA Style

Alireza Arabameri, Subodh Chandra Pal, Fatemeh Rezaie, Omid Asadi Nalivan, Indrajit Chowdhuri, Asish Saha, Saro Lee, Hossein Moayedi. Modeling groundwater potential using novel GIS-based machine-learning ensemble techniques. Journal of Hydrology: Regional Studies. 2021; 36 ():100848.

Chicago/Turabian Style

Alireza Arabameri; Subodh Chandra Pal; Fatemeh Rezaie; Omid Asadi Nalivan; Indrajit Chowdhuri; Asish Saha; Saro Lee; Hossein Moayedi. 2021. "Modeling groundwater potential using novel GIS-based machine-learning ensemble techniques." Journal of Hydrology: Regional Studies 36, no. : 100848.

Journal article
Published: 25 June 2021 in Minerals
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The Jangsu-gun area in the central Southwestern South Korea consists of a well-preserved Middle Paleoproterozoic gneissic basement, as well as the Late Triassic and Early Jurassic granitic rocks. Here, we present the detailed zircon U-Pb age data and whole-rock chemical compositions, including radioactive elements (e.g., U and Th) and activity concentrations of 226Ra, 232Th and 40K for the Middle Paleoproterozoic gneisses, and Late Triassic and Early Jurassic granitic rocks of the Jangsu-gun area. The Middle Paleoproterozoic gneissic basement, and the Late Triassic and Early Jurassic granitic rocks have ages of ca. 1988 Ma and 1824 Ma, 230 Ma and 187–189 Ma, respectively. Geochemically, the Middle Paleoproterozoic orthogneiss, Late Triassic granites and Early Jurassic granitic rocks show typical arc-related metaluminous to weakly peraluminous fractionated granite features with ASI (aluminum saturation index) values of 0.92 to 1.40. The mean values of U (ppm) and Th (ppm) of the Middle Paleoproterozoic orthogneisses (6.4 and 20.5, respectively), Late Triassic granites (1.5 and 10.9), and Early Jurassic granites (3.5 and 16.5) were similar to those (5 and 15) of the granitic rocks in the Earth’s crust. The mean 226Ra (Bq/kg), 232Th (Bq/kg), and 40K (Bq/kg) activity concentrations and radioactivity concentration index (RCI) are 62, 71, 1,214 and 0.96 for the Middle Paleoproterozoic orthogneisses; 16, 39, 1,614 and 0.78 for the Late Triassic granites; and 56, 70, 1031 and 0.88 for the Early Jurassic granitic rocks, respectively. The U, Th, 226Ra, 232Th, 40K, and RCI of the Middle Paleoproterozoic biotite paragneisses are similar to those of the Middle Paleoproterozoic orthogneisses. The trend of 226Ra, 232Th, and 40K activity concentrations, and the composition of U and Th from the Precambrian and Mesozoic rocks in the Jangsu-gun area indicates that monazite is the main accessory mineral controlling the concentration of natural radioactivity. Based on a detailed examination of the natural radioactivity in the rocks of the Jangsu-gun area, the Middle Paleoproterozoic orthogneisses and paragneisses, and Late Triassic and Early Jurassic granitic rocks show average high mean RCI values of 0.88−0.96, such that 32% of the rocks exceeded the recommended value of one in the guidelines for the RCI in South Korea. Especially, the RCI is closely related to the radon levels in the rocks. As a result, the Jangsu-gun area in South Korea is a relatively high radiological risk area, which exhibits higher indoor radon levels in the residences, compared with residences in the other areas in South Korea.

ACS Style

Sung Kim; Weon-Seo Kee; Saro Lee; Byung Lee; Uk Byun. Tracking and Evaluating the Concentrations of Natural Radioactivity According to Chemical Composition in the Precambrian and Mesozoic Granitic Rocks in the Jangsu-gun Area, Central Southwestern South Korea. Minerals 2021, 11, 684 .

AMA Style

Sung Kim, Weon-Seo Kee, Saro Lee, Byung Lee, Uk Byun. Tracking and Evaluating the Concentrations of Natural Radioactivity According to Chemical Composition in the Precambrian and Mesozoic Granitic Rocks in the Jangsu-gun Area, Central Southwestern South Korea. Minerals. 2021; 11 (7):684.

Chicago/Turabian Style

Sung Kim; Weon-Seo Kee; Saro Lee; Byung Lee; Uk Byun. 2021. "Tracking and Evaluating the Concentrations of Natural Radioactivity According to Chemical Composition in the Precambrian and Mesozoic Granitic Rocks in the Jangsu-gun Area, Central Southwestern South Korea." Minerals 11, no. 7: 684.

Editorial
Published: 26 May 2021 in Remote Sensing
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As computer and space technologies have developed, geoscience information systems (GIS) and remote sensing (RS) techniques have also been rapidly growing

ACS Style

Hyung-Sup Jung; Saro Lee. Remote Sensing and Geoscience Information Systems Applied to Groundwater Research. Remote Sensing 2021, 13, 2086 .

AMA Style

Hyung-Sup Jung, Saro Lee. Remote Sensing and Geoscience Information Systems Applied to Groundwater Research. Remote Sensing. 2021; 13 (11):2086.

Chicago/Turabian Style

Hyung-Sup Jung; Saro Lee. 2021. "Remote Sensing and Geoscience Information Systems Applied to Groundwater Research." Remote Sensing 13, no. 11: 2086.

Original research article
Published: 13 May 2021 in Frontiers in Earth Science
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The optimal prediction of land subsidence (LS) is very much difficult because of limitations in proper monitoring techniques, field-base surveys and knowledge related to functioning and behavior of LS. Thus, due to the lack of LS susceptibility maps it is almost impossible to identify LS prone areas and as a result it influences severe economic and human losses. Hence, preparation of LS susceptibility mapping (LSSM) can help to prevent natural and human catastrophes and reduce the economic damages significantly. Machine learning (ML) techniques are becoming increasingly proficient in modeling purpose of such kinds of occurrences and they are increasing used for LSSM. This study compares the performances of single and hybrid ML models to preparation of LSSM for future prediction of performance analysis. In this study, the spatial prediction of LS was assessed using four ML models of maximum entropy (MaxEnt), general linear model (GLM), artificial neural network (ANN) and support vector machine (SVM). Alongside, the possible numbers of novel ensemble models were integrated through the aforementioned four ML models for optimal analysis of LSSM. An inventory LS map was prepared based on the previous occurrences of LS points and the dataset were divvied into 70:30 ratios for training and validating of the modeling process. To identify the robust and best LSSMs, receiver operating characteristic-area under curve (ROC-AUC) curve was employed. The ROC-AUC result indicated that ANN model gives the highest ROC-AUC (0.924) in training accuracy. The highest AUC (0.823) of the LSSMs was determined based on validation datasets identified by SVM followed by ANN-SVM (0.812).

ACS Style

Alireza Arabameri; Saro Lee; Fatemeh Rezaie; Subodh Chandra Pal; Omid Asadi Nalivan; Asish Saha; Indrajit Chowdhuri; Hossein Moayedi. Performance Evaluation of GIS-Based Novel Ensemble Approaches for Land Subsidence Susceptibility Mapping. Frontiers in Earth Science 2021, 9, 1 .

AMA Style

Alireza Arabameri, Saro Lee, Fatemeh Rezaie, Subodh Chandra Pal, Omid Asadi Nalivan, Asish Saha, Indrajit Chowdhuri, Hossein Moayedi. Performance Evaluation of GIS-Based Novel Ensemble Approaches for Land Subsidence Susceptibility Mapping. Frontiers in Earth Science. 2021; 9 ():1.

Chicago/Turabian Style

Alireza Arabameri; Saro Lee; Fatemeh Rezaie; Subodh Chandra Pal; Omid Asadi Nalivan; Asish Saha; Indrajit Chowdhuri; Hossein Moayedi. 2021. "Performance Evaluation of GIS-Based Novel Ensemble Approaches for Land Subsidence Susceptibility Mapping." Frontiers in Earth Science 9, no. : 1.

Articles
Published: 05 May 2021 in Geocarto International
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Although the prediction of debris flow-prone areas represents a key step towards reducing damages, modeling debris flow susceptibility is complicated. In addition, the role of debris flow causal drivers in forested mountain landscapes are still poorly understood. To gain a holistic view of the causes of debris flows in the Umyeonsan, Seoul, South Korea region, we coupled the convolutional neural network (CNN) with two evolutionary optimization algorithms – grey wolf optimization (GWO) and cuckoo optimization algorithm (COA). Applying geoinformatics to debris flow factors, debris-flow susceptibility maps were generated and their validities were assessed with receiver operating characteristic (ROC) curves. The results reveal that three causative factors seem to contribute most to debris flows in the study area. The evolutionary optimization algorithms achieved higher goodness-of-fit and predictive power than the standalone CNN model. The goodness-of-fit and predictive skill measures of the CNN susceptibility map were 0.76 and 0.73. The values of CNN hybridized with GWO were 0.81 and 0.81 and hybridized with COA were 0.83 and 0.82. Slope degree, tree age, stream power index, geographical class, and soil drainage were the factors most affecting debris flow likelihood. The CNN-COA is the superior model and it predicted that 40.6% of the study area (i.e., 1844.96 km2) is highly and very highly susceptible to debris flows. The methodology can be applied for analysis of other region to improve risk management and guide development and land use planning.

ACS Style

Yang Li; Wei Chen; Fatemeh Rezaie; Omid Rahmati; Davoud Davoudi Moghaddam; John Tiefenbacher; Mahdi Panahi; Moung-Jin Lee; Dominik Kulakowski; Dieu Tien Bui; Saro Lee. Debris flows modeling using geo-environmental factors: developing hybridized deep-learning algorithms. Geocarto International 2021, 1 -25.

AMA Style

Yang Li, Wei Chen, Fatemeh Rezaie, Omid Rahmati, Davoud Davoudi Moghaddam, John Tiefenbacher, Mahdi Panahi, Moung-Jin Lee, Dominik Kulakowski, Dieu Tien Bui, Saro Lee. Debris flows modeling using geo-environmental factors: developing hybridized deep-learning algorithms. Geocarto International. 2021; ():1-25.

Chicago/Turabian Style

Yang Li; Wei Chen; Fatemeh Rezaie; Omid Rahmati; Davoud Davoudi Moghaddam; John Tiefenbacher; Mahdi Panahi; Moung-Jin Lee; Dominik Kulakowski; Dieu Tien Bui; Saro Lee. 2021. "Debris flows modeling using geo-environmental factors: developing hybridized deep-learning algorithms." Geocarto International , no. : 1-25.

Journal article
Published: 21 March 2021 in Remote Sensing
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The availability of groundwater is of concern. The demand for groundwater in Korea increased by more than 100% during the period 1994–2014. This problem will increase with population growth. Thus, a reliable groundwater analysis model for regional scale studies is needed. This study used the geographical information system (GIS) data and machine learning to map groundwater potential in Gangneung-si, South Korea. A spatial correlation performed using the frequency ratio was applied to determine the relationships between groundwater productivity (transmissivity data from 285 wells) and various factors. This study used four topography factors, four hydrological factors, and three geological factors, along with the normalized difference wetness index and land use and soil type. Support vector regression (SVR) and metaheuristic optimization algorithms—namely, grey wolf optimization (GWO), and particle swarm optimization (PSO), were used in the construction of the groundwater potential map. Model validation based on the area under the receiver operating curve (AUC) was used to determine model accuracy. The AUC values of groundwater potential maps made using the SVR, SVR_GWO, and SVR_PSO algorithms were 0.803, 0.878, and 0.814, respectively. Thus, the application of optimization algorithms increased model accuracy compared to the standard SVR algorithm. The findings of this study improve our understanding of groundwater potential in a given area and could be useful for policymakers aiming to manage water resources in the future.

ACS Style

Muhammad Fadhillah; Saro Lee; Chang-Wook Lee; Yu-Chul Park. Application of Support Vector Regression and Metaheuristic Optimization Algorithms for Groundwater Potential Mapping in Gangneung-si, South Korea. Remote Sensing 2021, 13, 1196 .

AMA Style

Muhammad Fadhillah, Saro Lee, Chang-Wook Lee, Yu-Chul Park. Application of Support Vector Regression and Metaheuristic Optimization Algorithms for Groundwater Potential Mapping in Gangneung-si, South Korea. Remote Sensing. 2021; 13 (6):1196.

Chicago/Turabian Style

Muhammad Fadhillah; Saro Lee; Chang-Wook Lee; Yu-Chul Park. 2021. "Application of Support Vector Regression and Metaheuristic Optimization Algorithms for Groundwater Potential Mapping in Gangneung-si, South Korea." Remote Sensing 13, no. 6: 1196.

Journal article
Published: 07 January 2021 in CATENA
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Flood spatial susceptibility prediction is the first essential step in developing flood mitigation strategies and reducing flood damage. Flood occurrence is a complex process that is not easily predicted through simple methods. This study describes optimization of support vector regression (SVR) using meta-optimization algorithms including the grasshopper optimization algorithm (GOA) and particle swarm optimization (PSO) for flood modeling at Qazvin Plain, Iran. Geospatial data including nine readily available geo-environmental flood conditioning factors (i.e., ground slope, aspect, elevation, planform curvature, profile curvature, proximity to a river, land use, lithology and rainfall) were derived. The information gain ratio (IGR) method was used to determine the relative importance of input variables. A historical flood inventory map for 43 locations was created from existing reports. The geospatial data and historical flood levels were used to construct the training and testing datasets. Then, the training dataset was used to generate flood-susceptibility maps using the optimized SVR model with the GOA and PSO algorithms. Finally, the predictive accuracy of the models was quantified using the statistical measures of root mean square error (RMSE), mean absolute error (MAE), and area under the receiver operating characteristic (ROC) curve (AUC). Although both the GOA and PSO algorithms improved SVR performance, the SVR-GOA model performed best (AUC = 0.959, RMSE = 0.31 and MSE = 0.098), followed by the SVR-PSO model (AUC = 0.959, RMSE = 0.33 and MSE = 0.11) and standalone SVR model (AUC = 0.87, RMSE = 0.35 and MSE = 0.12). Elevation, lithology and aspect had the highest IGR values and were identified as the most effective predictors of flood susceptibility.

ACS Style

Mahdi Panahi; Esmaeel Dodangeh; Fatemeh Rezaie; Khabat Khosravi; Hiep Van Le; Moung-Jin Lee; Saro Lee; Binh Thai Pham. Flood spatial prediction modeling using a hybrid of meta-optimization and support vector regression modeling. CATENA 2021, 199, 105114 .

AMA Style

Mahdi Panahi, Esmaeel Dodangeh, Fatemeh Rezaie, Khabat Khosravi, Hiep Van Le, Moung-Jin Lee, Saro Lee, Binh Thai Pham. Flood spatial prediction modeling using a hybrid of meta-optimization and support vector regression modeling. CATENA. 2021; 199 ():105114.

Chicago/Turabian Style

Mahdi Panahi; Esmaeel Dodangeh; Fatemeh Rezaie; Khabat Khosravi; Hiep Van Le; Moung-Jin Lee; Saro Lee; Binh Thai Pham. 2021. "Flood spatial prediction modeling using a hybrid of meta-optimization and support vector regression modeling." CATENA 199, no. : 105114.

Research article
Published: 01 January 2021 in Geomatics, Natural Hazards and Risk
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Spatial modelling of gully erosion at regional level is very relevant for local authorities to establish successful counter-measures and to change land-use planning. This work is exploring and researching the potential of a genetic algorithm-extreme gradient boosting (GE-XGBoost) hybrid computer education solution for spatial mapping of the susceptibility of gully erosion. The new machine learning approach is to combine the extreme gradient boosting machine (XGBoost) and the genetic algorithm (GA). The GA metaheuristic is being used to improve the efficiency of the XGBoost classification approach. A GIS database has been developed that contains recorded instances of gully erosion incidents and 18 conditioning variables. These parameters are used as predictive variables used to assess the condition of non-erosion or erosion in a given region within the Kohpayeh-Sagzi River Watershed research area in Iran. Exploratory results indicate that the proposed GE-XGBoost model is superior to the other benchmark solution with the desired predictive precision (89.56%). Therefore, the newly built model may be a promising method for large-scale mapping of gully erosion susceptibility.

ACS Style

Alireza Arabameri; Subodh Chandra Pal; Romulus Costache; Asish Saha; Fatemeh Rezaie; Amir Seyed Danesh; Biswajeet Pradhan; Saro Lee; Nhat-Duc Hoang. Prediction of gully erosion susceptibility mapping using novel ensemble machine learning algorithms. Geomatics, Natural Hazards and Risk 2021, 12, 469 -498.

AMA Style

Alireza Arabameri, Subodh Chandra Pal, Romulus Costache, Asish Saha, Fatemeh Rezaie, Amir Seyed Danesh, Biswajeet Pradhan, Saro Lee, Nhat-Duc Hoang. Prediction of gully erosion susceptibility mapping using novel ensemble machine learning algorithms. Geomatics, Natural Hazards and Risk. 2021; 12 (1):469-498.

Chicago/Turabian Style

Alireza Arabameri; Subodh Chandra Pal; Romulus Costache; Asish Saha; Fatemeh Rezaie; Amir Seyed Danesh; Biswajeet Pradhan; Saro Lee; Nhat-Duc Hoang. 2021. "Prediction of gully erosion susceptibility mapping using novel ensemble machine learning algorithms." Geomatics, Natural Hazards and Risk 12, no. 1: 469-498.

Journal article
Published: 16 December 2020 in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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Acute hydrological natural hazards such as floods not only affect the lives and properties of people but also causes severe damage to the critical infrastructures, which needs to be functioning even in substandard situations. Therefore, it is significant to predict the flood-prone areas for understanding how critical infrastructures are exposed to massive flooding. For this aim, flood-prone areas and their susceptibility is mapped using machine learning techniques including boosted regression tree (BRT) and generalized linear model (GLM) with the help of Sentinel 3 satellite images in Google Earth Engine. The BRT model achieved the highest precision (AUC=81.10%) in comparison to GLM (AUC=78.30%) and was utilized for evaluating the flood risk on critical infrastructures. In addition, the flood risk in four large and famous watersheds, namely, Zohre, Dorodzan, Tashk-Bakhtegan, and Qareagaj located in Fars Province was analyzed, among which Zohre had the highest risk to flooding. The assessment of flood risk on critical infrastructures such as hospitals, pharmacies, banks, fire stations, ATMs (automated teller machine), fuel stations, speed cameras, and mosques located in Shiraz County (capital of Fars Province) disclosed that these structures had high and very high flood hazard. The evaluation of flood risk on schools situated in nine most populated cities of Fars Province was also carried out, which revealed that Shiraz had the maximum percentage (92.98%) and Marvdasht had the least percentage (60.75%) of schools that fall under very high risk. This research aids the decision-makers in better planning for flood resilience and risk management.

ACS Style

Hamid Reza Pourghasemi; Mahdis Amiri; Mohsen Edalat; Amir Hossein Ahrari; Mahdi Panahi; Nitheshnirmal Sadhasivam; Saro Lee. Assessment of Urban Infrastructures Exposed to Flood Using Susceptibility Map and Google Earth Engine. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2020, 14, 1923 -1937.

AMA Style

Hamid Reza Pourghasemi, Mahdis Amiri, Mohsen Edalat, Amir Hossein Ahrari, Mahdi Panahi, Nitheshnirmal Sadhasivam, Saro Lee. Assessment of Urban Infrastructures Exposed to Flood Using Susceptibility Map and Google Earth Engine. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2020; 14 (99):1923-1937.

Chicago/Turabian Style

Hamid Reza Pourghasemi; Mahdis Amiri; Mohsen Edalat; Amir Hossein Ahrari; Mahdi Panahi; Nitheshnirmal Sadhasivam; Saro Lee. 2020. "Assessment of Urban Infrastructures Exposed to Flood Using Susceptibility Map and Google Earth Engine." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14, no. 99: 1923-1937.

Journal article
Published: 16 December 2020 in Geoscience Frontiers
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Flood probability maps are essential for a range of applications, including land use planning and developing mitigation strategies and early warning systems. This study describes the potential application of two architectures of deep learning neural networks, namely convolutional neural networks (CNN) and recurrent neural networks (RNN), for spatially explicit prediction and mapping of flash flood probability. To develop and validate the predictive models, a geospatial database that contained records for the historical flood events and geo-environmental characteristics of the Golestan Province in northern Iran was constructed. The step-wise weight assessment ratio analysis (SWARA) was employed to investigate the spatial interplay between floods and different influencing factors. The CNN and RNN models were trained using the SWARA weights and validated using the receiver operating characteristics technique. The results showed that the CNN model (AUC = 0.832, RMSE = 0.144) performed slightly better than the RNN model (AUC = 0.814, RMSE = 0.181) in predicting future floods. Further, these models demonstrated an improved prediction of floods compared to previous studies that used different models in the same study area. This study showed that the spatially explicit deep learning neural network models are successful in capturing the heterogeneity of spatial patterns of flood probability in the Golestan Province, and the resulting probability maps can be used for the development of mitigation plans in response to the future floods. The general policy implication of our study suggests that design, implementation, and verification of flood early warning systems should be directed to approximately 40% of the land area characterized by high and very susceptibility to flooding.

ACS Style

Mahdi Panahi; Abolfazl Jaafari; Ataollah Shirzadi; Himan Shahabi; Omid Rahmati; Ebrahim Omidvar; Saro Lee; Dieu Tien Bui. Deep learning neural networks for spatially explicit prediction of flash flood probability. Geoscience Frontiers 2020, 12, 101076 .

AMA Style

Mahdi Panahi, Abolfazl Jaafari, Ataollah Shirzadi, Himan Shahabi, Omid Rahmati, Ebrahim Omidvar, Saro Lee, Dieu Tien Bui. Deep learning neural networks for spatially explicit prediction of flash flood probability. Geoscience Frontiers. 2020; 12 (3):101076.

Chicago/Turabian Style

Mahdi Panahi; Abolfazl Jaafari; Ataollah Shirzadi; Himan Shahabi; Omid Rahmati; Ebrahim Omidvar; Saro Lee; Dieu Tien Bui. 2020. "Deep learning neural networks for spatially explicit prediction of flash flood probability." Geoscience Frontiers 12, no. 3: 101076.

Journal article
Published: 13 December 2020 in Geoscience Frontiers
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In this study, we developed multiple hybrid machine-learning models to address parameter optimization limitations and enhance the spatial prediction of landslide susceptibility models. We created a geographic information system database, and our analysis results were used to prepare a landslide inventory map containing 359 landslide events identified from Google Earth, aerial photographs, and other validated sources. A support vector regression (SVR) machine-learning model was used to divide the landslide inventory into training (70%) and testing (30%) datasets. The landslide susceptibility map was produced using 14 causative factors. We applied the established gray wolf optimization (GWO) algorithm, bat algorithm (BA), and cuckoo optimization algorithm (COA) to fine-tune the parameters of the SVR model to improve its predictive accuracy. The resultant hybrid models, SVR-GWO, SVR-BA, and SVR-COA, were validated in terms of the area under curve (AUC) and root mean square error (RMSE). The AUC values for the SVR-GWO (0.733), SVR-BA (0.724), and SVR-COA (0.738) models indicate their good prediction rates for landslide susceptibility modeling. SVR-COA had the greatest accuracy, with an RMSE of 0.21687, and SVR-BA had the least accuracy, with an RMSE of 0.23046. The three optimized hybrid models outperformed the SVR model (AUC = 0.704, RMSE = 0.26689), confirming the ability of metaheuristic algorithms to improve model performance.

ACS Style

Abdul-Lateef Balogun; Fatemeh Rezaie; Quoc Bao Pham; Ljubomir Gigović; Siniša Drobnjak; Yusuf A. Aina; Mahdi Panahi; Shamsudeen Temitope Yekeen; Saro Lee. Spatial prediction of landslide susceptibility in western Serbia using hybrid support vector regression (SVR) with GWO, BAT and COA algorithms. Geoscience Frontiers 2020, 12, 101104 .

AMA Style

Abdul-Lateef Balogun, Fatemeh Rezaie, Quoc Bao Pham, Ljubomir Gigović, Siniša Drobnjak, Yusuf A. Aina, Mahdi Panahi, Shamsudeen Temitope Yekeen, Saro Lee. Spatial prediction of landslide susceptibility in western Serbia using hybrid support vector regression (SVR) with GWO, BAT and COA algorithms. Geoscience Frontiers. 2020; 12 (3):101104.

Chicago/Turabian Style

Abdul-Lateef Balogun; Fatemeh Rezaie; Quoc Bao Pham; Ljubomir Gigović; Siniša Drobnjak; Yusuf A. Aina; Mahdi Panahi; Shamsudeen Temitope Yekeen; Saro Lee. 2020. "Spatial prediction of landslide susceptibility in western Serbia using hybrid support vector regression (SVR) with GWO, BAT and COA algorithms." Geoscience Frontiers 12, no. 3: 101104.

Journal article
Published: 19 November 2020 in Applied Sciences
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In recent years, the incidence of localized heavy rainfall has increased as abnormal weather events occur more frequently. In densely populated urban areas, this type of heavy rain can cause extreme landslide damage, so that it is necessary to estimate and analyze the susceptibility of future landslides. In this regard, deep learning (DL) methodologies have been used to identify areas prone to landslides recently. Therefore, in this study, DL methodologies, including a deep neural network (DNN), kernel-based DNN, and convolutional neural network (CNN) were used to identify areas where landslides could occur. As a detailed step for this purpose, landslide occurrence was first determined as landslide inventory through aerial photographs with comparative analysis using field survey data; a training set was built for model training through oversampling based on the landslide inventory. A total of 17 landslide influencing variables that influence the frequency of landslides by topography and geomorphology, as well as soil and forest variables, were selected to establish a landslide inventory. Then models were built using DNN, kernel-based DNN, and CNN models, and the susceptibility of landslides in the study area was determined. Model performance was evaluated through the average precision (AP) score and root mean square error (RMSE) for each of the three models. Finally, DNN, kernel-based DNN, and CNN models showed performances of 99.45%, 99.44%, and 99.41%, and RMSE values of 0.1694, 0.1806, and 0.1747, respectively. As a result, all three models showed similar performance, indicating excellent predictive ability of the models developed in this study. The information of landslides occurring in urban areas, which cause a great damage even with a small number of occurrences, can provide a basis for reference to the government and local authorities for urban landslide management.

ACS Style

Sunmin Lee; Won-Kyung Baek; Hyung-Sup Jung; Saro Lee. Susceptibility Mapping on Urban Landslides Using Deep Learning Approaches in Mt. Umyeon. Applied Sciences 2020, 10, 8189 .

AMA Style

Sunmin Lee, Won-Kyung Baek, Hyung-Sup Jung, Saro Lee. Susceptibility Mapping on Urban Landslides Using Deep Learning Approaches in Mt. Umyeon. Applied Sciences. 2020; 10 (22):8189.

Chicago/Turabian Style

Sunmin Lee; Won-Kyung Baek; Hyung-Sup Jung; Saro Lee. 2020. "Susceptibility Mapping on Urban Landslides Using Deep Learning Approaches in Mt. Umyeon." Applied Sciences 10, no. 22: 8189.

Journal article
Published: 10 November 2020 in Remote Sensing
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Gully formation through water-induced soil erosion and related to devastating land degradation is often a quasi-normal threat to human life, as it is responsible for huge loss of surface soil. Therefore, gully erosion susceptibility (GES) mapping is necessary in order to reduce the adverse effect of land degradation and diminishes this type of harmful consequences. The principle goal of the present research study is to develop GES maps for the Garhbeta I Community Development (C.D.) Block; West Bengal, India, by using a machine learning algorithm (MLA) of boosted regression tree (BRT), bagging and the ensemble of BRT-bagging with K-fold cross validation (CV) resampling techniques. The combination of the aforementioned MLAs with resampling approaches is state-of-the-art soft computing, not often used in GES evaluation. In further progress of our research work, here we used a total of 20 gully erosion conditioning factors (GECFs) and a total of 199 gully head cut points for modelling GES. The variables’ importance, which is responsible for gully erosion, was determined based on the random forest (RF) algorithm among the several GECFs used in this study. The output result of the model’s performance was validated through a receiver operating characteristics-area under curve (ROC-AUC), sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) statistical analysis. The predicted result shows that the ensemble of BRT-bagging is the most well fitted for GES where AUC value in K-3 fold is 0.972, whereas the value of AUC in sensitivity, specificity, PPV and NPV is 0.94, 0.93, 0.96 and 0.93, respectively, in a training dataset, and followed by the bagging and BRT model. Thus, from the predictive performance of this research study it is concluded that the ensemble of BRT-Bagging can be applied as a new approach for further studies in spatial prediction of GES. The outcome of this work can be helpful to policy makers in implementing remedial measures to minimize damages caused by gully erosion.

ACS Style

Subodh Pal; Alireza Arabameri; Thomas Blaschke; Indrajit Chowdhuri; Asish Saha; Rabin Chakrabortty; Saro Lee; Shahab. Band. Ensemble of Machine-Learning Methods for Predicting Gully Erosion Susceptibility. Remote Sensing 2020, 12, 3675 .

AMA Style

Subodh Pal, Alireza Arabameri, Thomas Blaschke, Indrajit Chowdhuri, Asish Saha, Rabin Chakrabortty, Saro Lee, Shahab. Band. Ensemble of Machine-Learning Methods for Predicting Gully Erosion Susceptibility. Remote Sensing. 2020; 12 (22):3675.

Chicago/Turabian Style

Subodh Pal; Alireza Arabameri; Thomas Blaschke; Indrajit Chowdhuri; Asish Saha; Rabin Chakrabortty; Saro Lee; Shahab. Band. 2020. "Ensemble of Machine-Learning Methods for Predicting Gully Erosion Susceptibility." Remote Sensing 12, no. 22: 3675.

Preprint content
Published: 09 November 2020
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Task allocation in uncertainty conditions is a key problem for agents attempting to achieve harmony in disaster environments. This paper presents an agent- based simulation to investigate tasks allocation through the consideration of appropriate spatial strategies to deal with uncertainty in urban search and rescue (USAR) operation. The proposed method is presented in five phases: ordering existing tasks, finding coordinating agent, holding an auction, applying allocation strategies, and implementation and observation of environmental uncertainties. The methodology was evaluated in Tehran's District 1 for 6.6, 6.9, and 7.2 magnitude earthquakes. The simulation started by calculating the number of injured individuals, which was 28856, 73195 and 111463 people for each earthquake, respectively. The Simulations were performed for each scenario for a variety of rescuers (1000, 1500, 2000 rescuer). In comparison with contract net protocol (CNP), the standard time of rescue operations in the proposed approach includes at least 13% of improvement and the best percentage of recovery was 21 %. Interval uncertainty analysis and the comparison of the proposed strategies showed that an increase in uncertainty leads to an increased rescue time for CNP of 67.7 hours, and for strategies one to four an increased rescue time of 63.4, 63.2, 63.7, and 56.5 hours, respectively. Considering strategies in the task allocation process, especially spatial strategies, resulted in the optimization and increased flexibility of the allocation as well as conditions for fault tolerance and agent-based cooperation stability in USAR simulation system.

ACS Style

Navid Hooshangi; Ali Asghar Alesheikh; Mahdi Panahi; Saro Lee. USAR simulation system: presenting spatial strategies in agents' task allocation under uncertainties. 2020, 2020, 1 -18.

AMA Style

Navid Hooshangi, Ali Asghar Alesheikh, Mahdi Panahi, Saro Lee. USAR simulation system: presenting spatial strategies in agents' task allocation under uncertainties. . 2020; 2020 ():1-18.

Chicago/Turabian Style

Navid Hooshangi; Ali Asghar Alesheikh; Mahdi Panahi; Saro Lee. 2020. "USAR simulation system: presenting spatial strategies in agents' task allocation under uncertainties." 2020, no. : 1-18.

Journal article
Published: 16 October 2020 in Remote Sensing
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Landslides are natural and often quasi-normal threats that destroy natural resources and may lead to a persistent loss of human life. Therefore, the preparation of landslide susceptibility maps is necessary in order to mitigate harmful effects. The key objective of this research is to develop landslide susceptibility maps for the Taleghan basin of Alborz province, Iran, using hybrid Machine Learning (ML) algorithms, i.e., k-fold cross validation and ML techniques of credal decision tree (CDT), Alternative Decision Tree (ADTree), and their ensemble method (CDT-ADTree), which have been state-of-the-art soft computing techniques rarely used in the case of landslide susceptibility assessments. In this study, 22 key landslide causative factors (LCFs) were considered to explore their spatial relationship to landslides, based on local geomorphological and geo-environmental influences. The Random Forest (RF) algorithm was used for the identification of variables importance of different LCFs that are more prone to landslide susceptibility. A receiver operation characteristics (ROC) curve with area under the curve (AUC), accuracy, precision, and robustness index was used to evaluate and compare landslide susceptibility models. The output of the model performance shows that the CDT-ADTree model is the more robust model for the landslide susceptibility where the AUC, accuracy, and precision are 0.981, 0.837, and 0.867, respectively, than the standalone model of CDT and ADTree model. Therefore, it is concluded that the CDT-ADTree ensemble model can be applied as a new promising technique for spatial prediction of the landslide in further studies.

ACS Style

Alireza Arabameri; Ebrahim Karimi-Sangchini; Subodh Pal; Asish Saha; Indrajit Chowdhuri; Saro Lee; Dieu Tien Bui. Novel Credal Decision Tree-Based Ensemble Approaches for Predicting the Landslide Susceptibility. Remote Sensing 2020, 12, 3389 .

AMA Style

Alireza Arabameri, Ebrahim Karimi-Sangchini, Subodh Pal, Asish Saha, Indrajit Chowdhuri, Saro Lee, Dieu Tien Bui. Novel Credal Decision Tree-Based Ensemble Approaches for Predicting the Landslide Susceptibility. Remote Sensing. 2020; 12 (20):3389.

Chicago/Turabian Style

Alireza Arabameri; Ebrahim Karimi-Sangchini; Subodh Pal; Asish Saha; Indrajit Chowdhuri; Saro Lee; Dieu Tien Bui. 2020. "Novel Credal Decision Tree-Based Ensemble Approaches for Predicting the Landslide Susceptibility." Remote Sensing 12, no. 20: 3389.

Journal article
Published: 10 October 2020 in Remote Sensing
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The extreme form of land degradation through different forms of erosion is one of the major problems in sub-tropical monsoon dominated region. The formation and development of gullies is the dominant form or active process of erosion in this region. So, identification of erosion prone regions is necessary for escaping this type of situation and maintaining the correspondence between different spheres of the environment. The major goal of this study is to evaluate the gully erosion susceptibility in the rugged topography of the Hinglo River Basin of eastern India, which ultimately contributes to sustainable land management practices. Due to the nature of data instability, the weakness of the classifier andthe ability to handle data, the accuracy of a single method is not very high. Thus, in this study, a novel resampling algorithm was considered to increase the robustness of the classifier and its accuracy. Gully erosion susceptibility maps have been prepared using boosted regression trees (BRT), multivariate adaptive regression spline (MARS) and spatial logistic regression (SLR) with proposed resampling techniques. The re-sampling algorithm was able to increase the efficiency of all predicted models by improving the nature of the classifier. Each variable in the gully inventory map was randomly allocated with 5-fold cross validation, 10-fold cross validation, bootstrap and optimism bootstrap, while each consisted of 30% of the database. The ensemble model was tested using 70% and validated with the other 30% using the K-fold cross validation (CV) method to evaluate the influence of the random selection of training and validation database. Here, all resampling methods are associated with higher accuracy, but SLR bootstrap optimism is more optimal than any other methods according to its robust nature. The AUC values of BRT optimism bootstrap, MARS optimism bootstrap and SLR optimism bootstrap are 87.40%, 90.40% and 90.60%, respectively. According to the SLR optimism bootstrap, the 107,771 km2 (27.51%) area of this region is associated with a very high to high susceptible to gully erosion. This potential developmental area of the gully was found primarily in the Hinglo River Basin, where lateral exposure was mainly observed with scarce vegetation. The outcome of this work can help policy-makers to implement remedial measures to minimize the damage caused by erosion of the gully.

ACS Style

Paramita Roy; Subodh Chandra Pal; Alireza Arabameri; Rabin Chakrabortty; Biswajeet Pradhan; Indrajit Chowdhuri; Saro Lee; Dieu Tien Bui. Novel Ensemble of Multivariate Adaptive Regression Spline with Spatial Logistic Regression and Boosted Regression Tree for Gully Erosion Susceptibility. Remote Sensing 2020, 12, 3284 .

AMA Style

Paramita Roy, Subodh Chandra Pal, Alireza Arabameri, Rabin Chakrabortty, Biswajeet Pradhan, Indrajit Chowdhuri, Saro Lee, Dieu Tien Bui. Novel Ensemble of Multivariate Adaptive Regression Spline with Spatial Logistic Regression and Boosted Regression Tree for Gully Erosion Susceptibility. Remote Sensing. 2020; 12 (20):3284.

Chicago/Turabian Style

Paramita Roy; Subodh Chandra Pal; Alireza Arabameri; Rabin Chakrabortty; Biswajeet Pradhan; Indrajit Chowdhuri; Saro Lee; Dieu Tien Bui. 2020. "Novel Ensemble of Multivariate Adaptive Regression Spline with Spatial Logistic Regression and Boosted Regression Tree for Gully Erosion Susceptibility." Remote Sensing 12, no. 20: 3284.

Journal article
Published: 29 September 2020 in ISPRS International Journal of Geo-Information
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Landslides can cause considerable loss of life and damage to property, and are among the most frequent natural hazards worldwide. One of the most fundamental and simple approaches to reduce damage is to prepare a landslide hazard map. Accurate prediction of areas highly prone to future landslides is important for decision-making. In the present study, for the first time, the group method of data handling (GMDH) was used to generate landslide susceptibility map for a specific region in Uzbekistan. First, 210 landslide locations were identified by field survey and then divided randomly into model training and model validation datasets (70% and 30%, respectively). Data on nine conditioning factors, i.e., altitude, slope, aspect, topographic wetness index (TWI), length of slope (LS), valley depth, distance from roads, distance from rivers, and geology, were collected. Finally, the maps were validated using the testing dataset and receiver operating characteristic (ROC) curve analysis. The findings showed that the “optimized” GMDH model (i.e., using the gray wolf optimizer [GWO]) performed better than the standalone GMDH model, during both the training and testing phase. The accuracy of the GMDH–GWO model in the training and testing phases was 94% and 90%, compared to 85% and 82%, respectively, for the standard GMDH model. According to the GMDH–GWO model, the study area included very low, low, moderate, high, and very high landslide susceptibility areas, with proportions of 14.89%, 10.57%, 15.00%, 35.12%, and 24.43%, respectively.

ACS Style

Azam Kadirhodjaev; Fatemeh Rezaie; Moung-Jin Lee; Saro Lee. Landslide Susceptibility Assessment Using an Optimized Group Method of Data Handling Model. ISPRS International Journal of Geo-Information 2020, 9, 566 .

AMA Style

Azam Kadirhodjaev, Fatemeh Rezaie, Moung-Jin Lee, Saro Lee. Landslide Susceptibility Assessment Using an Optimized Group Method of Data Handling Model. ISPRS International Journal of Geo-Information. 2020; 9 (10):566.

Chicago/Turabian Style

Azam Kadirhodjaev; Fatemeh Rezaie; Moung-Jin Lee; Saro Lee. 2020. "Landslide Susceptibility Assessment Using an Optimized Group Method of Data Handling Model." ISPRS International Journal of Geo-Information 9, no. 10: 566.

Journal article
Published: 20 September 2020 in Journal of Hydrology
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Iran experiences frequent destructive floods with significant socioeconomic consequences. Quantifying the accurate impacts of such natural hazards, however, is a complicated task. The present study uses a deep learning convolutional neural networks (CNN) algorithm, which is among the newer and most powerful algorithms in big data sets, to develop a flood susceptibility map for Iran. A total of 2769 records were collected from flood locations across the entire country; we divided this data set into two groups using a cross-validation technique. The first group, used as a training data set, was constructed from 70% of the data set and was used for model building. The second group, used as a testing data set, was constructed from the remaining 30% of the records and used for validation. Ten flood conditioning factors, slope, altitude, aspect, plan curvature, profile curvature, rainfall, geology, land use, distance from roads, and distance from rivers, were identified and used in the modeling process. The area under the prediction-rate curve was used for model evaluation, with results showing that the flood susceptibility map has an acceptable accuracy of 75%. The results also indicated that approximately 12% and 3% of the country are highly and very highly susceptible to future flooding events, respectively. Moreover, 29% and 49% of Iran’s cities are located in areas with high and very high susceptibility to future flooding hazards. The most effective approaches to flood mitigation are preventing urban expansion and new construction in highly to very highly flood-prone areas as well as watershed management plans and constructing flood control structures according to the topographical characteristics of the catchment.

ACS Style

Khabat Khosravi; Mahdi Panahi; Ali Golkarian; Saskia D. Keesstra; Patricia M. Saco; Dieu Tien Bui; Saro Lee. Convolutional neural network approach for spatial prediction of flood hazard at national scale of Iran. Journal of Hydrology 2020, 591, 125552 .

AMA Style

Khabat Khosravi, Mahdi Panahi, Ali Golkarian, Saskia D. Keesstra, Patricia M. Saco, Dieu Tien Bui, Saro Lee. Convolutional neural network approach for spatial prediction of flood hazard at national scale of Iran. Journal of Hydrology. 2020; 591 ():125552.

Chicago/Turabian Style

Khabat Khosravi; Mahdi Panahi; Ali Golkarian; Saskia D. Keesstra; Patricia M. Saco; Dieu Tien Bui; Saro Lee. 2020. "Convolutional neural network approach for spatial prediction of flood hazard at national scale of Iran." Journal of Hydrology 591, no. : 125552.