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Wahba M, Essam R, El-Rawy M, Al-Arifi N, Abdalla F, Elsadek WM. Forecasting of flash flood susceptibility mapping using random forest regression model and geographic information systems. Heliyon 2024; 10:e33982. [PMID: 39071561 PMCID: PMC11282991 DOI: 10.1016/j.heliyon.2024.e33982] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Revised: 07/01/2024] [Accepted: 07/01/2024] [Indexed: 07/30/2024] Open
Abstract
Flash floods, rapid and devastating inundations of water, are increasingly linked to the intensifying effects of climate change, posing significant challenges for both vulnerable communities and sustainable environmental management. The primary goal of this research is to investigate and predict a Flood Susceptibility Map (FSM) for the Ibaraki prefecture in Japan. This research utilizes a Random Forest (RF) regression model and GIS, incorporating 11 environmental variables (involving elevation, slope, aspect, distance to stream, distance to river, distance to road, land cover, topographic wetness index, stream power index, and plan and profile curvature), alongside a dataset comprising 224 instances of flooded and non-flooded locations. The data was randomly classified into a 70 % training set for model development, with the remaining 30 % used for model validation through Receiver Operating Characteristics (ROC) curve analysis. The resulting map indicated that approximately two-thirds of the prefecture as exhibiting low to very low flood susceptibility, while approximately one-fifth of the region is categorized as high to very high flood susceptibility. Furthermore, the RF model achieved a noteworthy validation with an area under the ROC curve of 99.56 %. Ultimately, this FSM serves as a crucial tool for policymakers in guiding appropriate spatial planning and flood mitigation strategies.
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Affiliation(s)
- Mohamed Wahba
- Civil Engineering Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt
| | - Radwa Essam
- Mathematics and Engineering Physics Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt
| | - Mustafa El-Rawy
- Civil Engineering Department, Faculty of Engineering, Minia University, Minia, 61111, Egypt
- Civil Engineering Department, College of Engineering, Shaqra University, Dawadmi, 11911, Saudi Arabia
| | - Nassir Al-Arifi
- Chair of Natural Hazards and Mineral Resources, Geology and Geophysics Department, King Saud University, Riyadh, 11451, Saudi Arabia
| | - Fathy Abdalla
- Deanship of Scientific Research, King Saud University, Riyadh, 145111, Saudi Arabia
| | - Wael M. Elsadek
- Civil Engineering Department, Faculty of Engineering, South Valley University, Qena, 83521, Egypt
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Sarkar SK, Rudra RR, Talukdar S, Das PC, Nur MS, Alam E, Islam MK, Islam ARMT. Future groundwater potential mapping using machine learning algorithms and climate change scenarios in Bangladesh. Sci Rep 2024; 14:10328. [PMID: 38710767 DOI: 10.1038/s41598-024-60560-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Accepted: 04/24/2024] [Indexed: 05/08/2024] Open
Abstract
The aim of the study was to estimate future groundwater potential zones based on machine learning algorithms and climate change scenarios. Fourteen parameters (i.e., curvature, drainage density, slope, roughness, rainfall, temperature, relative humidity, lineament density, land use and land cover, general soil types, geology, geomorphology, topographic position index (TPI), topographic wetness index (TWI)) were used in developing machine learning algorithms. Three machine learning algorithms (i.e., artificial neural network (ANN), logistic model tree (LMT), and logistic regression (LR)) were applied to identify groundwater potential zones. The best-fit model was selected based on the ROC curve. Representative concentration pathways (RCP) of 2.5, 4.5, 6.0, and 8.5 climate scenarios of precipitation were used for modeling future climate change. Finally, future groundwater potential zones were identified for 2025, 2030, 2035, and 2040 based on the best machine learning model and future RCP models. According to findings, ANN shows better accuracy than the other two models (AUC: 0.875). The ANN model predicted that 23.10 percent of the land was in very high groundwater potential zones, whereas 33.50 percent was in extremely high groundwater potential zones. The study forecasts precipitation values under different climate change scenarios (RCP2.6, RCP4.5, RCP6, and RCP8.5) for 2025, 2030, 2035, and 2040 using an ANN model and shows spatial distribution maps for each scenario. Finally, sixteen scenarios were generated for future groundwater potential zones. Government officials may utilize the study's results to inform evidence-based choices on water management and planning at the national level.
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Affiliation(s)
- Showmitra Kumar Sarkar
- Department of Urban and Regional Planning, Khulna University of Engineering & Technology (KUET), Khulna, 9203, Bangladesh.
| | - Rhyme Rubayet Rudra
- Department of Urban and Regional Planning, Khulna University of Engineering & Technology (KUET), Khulna, 9203, Bangladesh
| | - Swapan Talukdar
- Department of Geography, Asutosh College, University of Calcutta, Kolkata, 700026, India
| | - Palash Chandra Das
- Department of Urban and Regional Planning, Khulna University of Engineering & Technology (KUET), Khulna, 9203, Bangladesh
- Department of Geography, Texas A&M University, College Station, USA
| | - Md Sadmin Nur
- Department of Urban and Regional Planning, Khulna University of Engineering & Technology (KUET), Khulna, 9203, Bangladesh
| | - Edris Alam
- Faculty of Resilience, Rabdan Academy, 22401, Abu Dhabi, United Arab Emirates
- Department of Geography and Environmental Studies, University of Chittagong, Chittagong, 4331, Bangladesh
| | - Md Kamrul Islam
- Department of Civil and Environmental Engineering, College of Engineering, King Faisal University, AlAhsa, 31982, Saudi Arabia
| | - Abu Reza Md Towfiqul Islam
- Department of Disaster Management, Begum Rokeya University, Rangpur, 5400, Bangladesh
- Department of Development Studies, Daffodil International University, Dhaka, 1216, Bangladesh
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Sarkar SK, Rudra RR, Santo MMH. Cyclone vulnerability assessment in the coastal districts of Bangladesh. Heliyon 2024; 10:e23555. [PMID: 38192777 PMCID: PMC10772640 DOI: 10.1016/j.heliyon.2023.e23555] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 11/13/2023] [Accepted: 12/06/2023] [Indexed: 01/10/2024] Open
Abstract
This research aims to assess the vulnerability to cyclones in the coastal regions of Bangladesh, employing a comprehensive framework derived from the Intergovernmental Panel on Climate Change (IPCC, 2007). The study considers a total of eighteen factors, categorized into three critical dimensions: exposure, sensitivity, and adaptive capacity. These factors are crucial in understanding the potential impact of cyclones in the region. In order to develop a cyclone vulnerability map, Principal Component Analysis (PCA) was applied, primarily focusing on the dimensions of sensitivity and adaptive capacity. The findings of this analysis revealed that sensitivity and adaptive capacity components accounted for a significant percentage of variance in the data, explaining 90.00 % and 90.93 % of the variance, respectively. Despite the lack of details about data collection, the study identified specific factors contributing significantly to each dimension. Notably, proximity to the coastline emerged as a highly influential factor in determining cyclone exposure. The results of this research indicate that certain areas, such as Jessore, Khulna, Narail, Gopalgonj, and Bagerhat, exhibit low exposure to cyclones, whereas regions like Chandpur and Lakshmipur face a high level of exposure. Sensitivity was found to be high in most areas, with Noakhali, Lakshmipur, and Chandpur being the most sensitive regions. Adaptive capacity was observed to vary significantly, with low values near the sea, particularly in locations like Cox's Bazar, Shatkhira, Bagerhat, Noakhali, and Bhola, and high values in regions farther from the coast. Overall, vulnerability to cyclones was found to be very high in Noakhali, Lakshmipur, Chandpur, and Bhola, low in Jessore and Khulna, and moderate in Barisal, Narail, Gopalgonj, and Jhalokati. These findings are expected to provide valuable insights to inform decision-makers and authorities tasked with managing the consequences of cyclones in the region.
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Affiliation(s)
- Showmitra Kumar Sarkar
- Department of Urban and Regional Planning, Khulna University of Engineering & Technology, Khulna-9203, Bangladesh
| | - Rhyme Rubayet Rudra
- Department of Urban and Regional Planning, Khulna University of Engineering & Technology, Khulna-9203, Bangladesh
| | - Md. Mehedi Hasan Santo
- Department of Urban and Regional Planning, Khulna University of Engineering & Technology, Khulna-9203, Bangladesh
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Rash A, Mustafa Y, Hamad R. Quantitative assessment of Land use/land cover changes in a developing region using machine learning algorithms: A case study in the Kurdistan Region, Iraq. Heliyon 2023; 9:e21253. [PMID: 37954393 PMCID: PMC10638604 DOI: 10.1016/j.heliyon.2023.e21253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 10/13/2023] [Accepted: 10/18/2023] [Indexed: 11/14/2023] Open
Abstract
The identification of land use/land cover (LULC) changes is important for monitoring, evaluating, and preserving natural resources. In the Kurdistan region, the utilization of remotely sensed data to assess the effectiveness of machine learning algorithms (MLAs) for LULC classification and change detection analysis has been limited. This study monitors and analyzes LULC changes in the study area from 1991 to 2021 using a quantitative approach with multi-temporal Landsat imagery. Five MLAs were applied: Support Vector Machine (SVM), Random Forest (RF), Artificial Neural Network (ANN), K-Nearest Neighbor (KNN), and Extreme Gradient Boosting (XGBoost). The results showed that the RF algorithm produced the most accurate maps of the three-decade study period, accompanied by a high kappa coefficient (0.93-0.97) compared with the SVM (0.91-0.95), ANN (0.91-0.96), KNN (0.92-0.96), and XGBoost (0.92-0.95) algorithms. Consequently, the RF classifier was implemented to categorize all obtainable satellite images. Socioeconomic changes throughout these transition periods were revealed by the change detection results. Rangeland and barren land areas decreased by 11.33 % (-402.03 km2) and 6.68 % (-236.8 km2), respectively. The transmission increases of 13.54 % (480.18 km2), 3.43 % (151.74 km2), and 0.71 % (25.22 km2) occurred in agricultural land, forest, and built-up areas, respectively. The outcomes of this study contribute significantly to LULC monitoring in developing regions, guiding stakeholders to identify vulnerable areas for better land use planning and sustainable environmental protection.
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Affiliation(s)
- Abdulqadeer Rash
- Dept. of Petroleum Geosciences, Faculty of Science, Soran University, 44008, Soran, Erbil, Iraq
- Soran Research Centre, Soran University, Soran, Erbil, Iraq
| | - Yaseen Mustafa
- Dept. of Environmental Sciences, Faculty of Science, University of Zakho, Duhok, Iraq
| | - Rahel Hamad
- Dept. of Petroleum Geosciences, Faculty of Science, Soran University, 44008, Soran, Erbil, Iraq
- Soran Research Centre, Soran University, Soran, Erbil, Iraq
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Sarkar SK, Rudra RR, Sohan AR, Das PC, Ekram KMM, Talukdar S, Rahman A, Alam E, Islam MK, Islam ARMT. Coupling of machine learning and remote sensing for soil salinity mapping in coastal area of Bangladesh. Sci Rep 2023; 13:17056. [PMID: 37816754 PMCID: PMC10564761 DOI: 10.1038/s41598-023-44132-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 10/04/2023] [Indexed: 10/12/2023] Open
Abstract
Soil salinity is a pressing issue for sustainable food security in coastal regions. However, the coupling of machine learning and remote sensing was seldom employed for soil salinity mapping in the coastal areas of Bangladesh. The research aims to estimate the soil salinity level in a southwestern coastal region of Bangladesh. Using the Landsat OLI images, 13 soil salinity indicators were calculated, and 241 samples of soil salinity data were collected from a secondary source. This study applied three distinct machine learning models (namely, random forest, bagging with random forest, and artificial neural network) to estimate soil salinity. The best model was subsequently used to categorize soil salinity zones into five distinct groups. According to the findings, the artificial neural network model has the highest area under the curve (0.921), indicating that it has the most potential to predict and detect soil salinity zones. The high soil salinity zone covers an area of 977.94 km2 or roughly 413.51% of the total study area. According to additional data, a moderate soil salinity zone (686.92 km2) covers 30.56% of Satkhira, while a low soil salinity zone (582.73 km2) covers 25.93% of the area. Since increased soil salinity adversely affects human health, agricultural production, etc., the study's findings will be an effective tool for policymakers in integrated coastal zone management in the southwestern coastal area of Bangladesh.
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Affiliation(s)
- Showmitra Kumar Sarkar
- Department of Urban and Regional Planning, Khulna University of Engineering & Technology (KUET), Khulna, 9203, Bangladesh.
| | - Rhyme Rubayet Rudra
- Department of Urban and Regional Planning, Khulna University of Engineering & Technology (KUET), Khulna, 9203, Bangladesh
| | - Abid Reza Sohan
- Department of Urban and Regional Planning, Khulna University of Engineering & Technology (KUET), Khulna, 9203, Bangladesh
| | - Palash Chandra Das
- Department of Urban and Regional Planning, Khulna University of Engineering & Technology (KUET), Khulna, 9203, Bangladesh
- Department of Geography, Texas A&M University, College Station, USA
| | - Khondaker Mohammed Mohiuddin Ekram
- Department of Urban and Regional Planning, Khulna University of Engineering & Technology (KUET), Khulna, 9203, Bangladesh
- Population Health Sciences, Harvard University, Cambridge, USA
| | - Swapan Talukdar
- Department of Geography, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi, 110025, India
| | - Atiqur Rahman
- Department of Geography, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi, 110025, India
| | - Edris Alam
- Faculty of Resilience, Rabdan Academy, 22401, Abu Dhabi, United Arab Emirates
- Department of Geography and Environmental Studies, University of Chittagong, Chittagong, 4331, Bangladesh
| | - Md Kamrul Islam
- Department of Civil and Environmental Engineering, College of Engineering, King Faisal University, 31982, AlAhsa, Saudi Arabia
| | - Abu Reza Md Towfiqul Islam
- Department of Disaster Management, Begum Rokeya University, Rangpur, 5400, Bangladesh
- Department of Development Studies, Daffodil International University, Dhaka, 1216, Bangladesh
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