Investigation and Evaluation of Land Use–Land Cover Change Effects on Current and Future Flood Susceptibility
Abstract
One of the most remarkable factors affecting the flood susceptibility in watersheds is the change in land use and land cover (LULC). Moreover, machine learning (ML) methods showed reliable efficiency in predicting LULC and preparing flood susceptibility maps. Therefore, this study aims to predict LULC of the future using land change modeler (LCM), and then investigate the effects of LULC changes on the flood susceptibility map using ML algorithms in the Talar watershed, Iran. In this regard, LCM model based on artificial neural network (ANN) and Markov chain analysis used to predict LULC in the future. Then, the effect of LULC changes on flood susceptibility was investigated using some ML algorithms, including ANN, logistic regression (LR), random forest (RF), and support vector machine (SVM). These four algorithms were used for the first time simultaneously to generate flood susceptibility maps in the present and future. Nine influencing factors, including altitude, slope, distance from rivers, land use and land cover, topographic wetness index, stream power index, profile curvature, rainfall, soil type, and 200 flood and nonflood locations, were utilized to create the flood susceptibility map. The selected ML models were assessed using root mean square error (RMSE) and coefficient of determination (). RMSE values showed high performance of SVM, LR, RF, and ANN models with values of 0.125, 0.175, 0.1920, and 0.2834, respectively. Furthermore, values of those models were computed as 0.9373, 0.8773, 0.8667, and 0.8525, respectively. Overall, the findings demonstrated the performance of the LCM method in forecasting the LULC map and the capability of the selected ML algorithms to produce flood susceptibility maps in the study area. In cities and watersheds all over the world that are dealing with LULC changes and a possibility of flooding, the findings and algorithms used in this study can be applied. This aids officials and decision makers to make more effective and efficient decisions in flood prevention and management.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
All data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
References
Alamdari, N., P. Claggett, D. J. Sample, Z. M. Easton, and M. N. Yazdi. 2022. “Evaluating the joint effects of climate and land use change on runoff and pollutant loading in a rapidly developing watershed.” J. Cleaner Prod. 330 (Jan): 129953. https://doi.org/10.1016/j.jclepro.2021.129953.
Alexakis, D., M. Grillakis, A. G. Koutroulis, A. Agapiou, K. Themistocleous, I. Tsanis, S. Michaelides, S. Pashiardis, C. Demetriou, and K. Aristeidou. 2014. “GIS and remote sensing techniques for the assessment of land use change impact on flood hydrology: The case study of Yialias basin in Cyprus.” Nat. Hazards Earth Syst. Sci. 14 (2): 413–426. https://doi.org/10.5194/nhess-14-413-2014.
Andaryani, S., V. Nourani, A. T. Haghighi, and S. Keesstra. 2021. “Integration of hard and soft supervised machine learning for flood susceptibility mapping.” J. Environ. Manage. 291 (Aug): 112731. https://doi.org/10.1016/j.jenvman.2021.112731.
Apollonio, C., G. Balacco, A. Novelli, E. Tarantino, and A. F. Piccinni. 2016. “Land use change impact on flooding areas: The case study of Cervaro Basin (Italy).” Sustainability 8 (10): 996. https://doi.org/10.3390/su8100996.
Arabameri, A., S. Saha, W. Chen, J. Roy, B. Pradhan, and D. T. Bui. 2020. “Flash flood susceptibility modelling using functional tree and hybrid ensemble techniques.” J. Hydrol. 587 (Aug): 125007. https://doi.org/10.1016/j.jhydrol.2020.125007.
Arora, A., A. Arabameri, M. Pandey, M. A. Siddiqui, U. Shukla, D. T. Bui, V. N. Mishra, and A. Bhardwaj. 2021. “Optimization of state-of-the-art fuzzy-metaheuristic ANFIS-based machine learning models for flood susceptibility prediction mapping in the Middle Ganga Plain, India.” Sci. Total Environ. 750 (Jan): 141565. https://doi.org/10.1016/j.scitotenv.2020.141565.
Avand, M., and H. Moradi. 2021. “Using machine learning models, remote sensing, and GIS to investigate the effects of changing climates and land uses on flood probability.” J. Hydrol. 595 (Aug): 125663. https://doi.org/10.1016/j.jhydrol.2020.125663.
Beven, K. J., and M. J. Kirkby. 1979. “A physically based, variable contributing area model of basin hydrology/Un modèle à base physique de zone d’appel variable de l’hydrologie du bassin versant.” Hydrol. Sci. J. 24 (1): 43–69. https://doi.org/10.1080/02626667909491834.
Breiman, L. 2001. “Random forests.” Mach. Learn. 45 (1): 5–32. https://doi.org/10.1023/A:1010933404324.
Bui, D. T., P. Tsangaratos, P.-T. T. Ngo, T. D. Pham, and B. T. Pham. 2019. “Flash flood susceptibility modeling using an optimized fuzzy rule based feature selection technique and tree based ensemble methods.” Sci. Total Environ. 668 (Apr): 1038–1054. https://doi.org/10.1016/j.scitotenv.2019.02.422.
Chen, J., G. Huang, and W. Chen. 2021. “Towards better flood risk management: Assessing flood risk and investigating the potential mechanism based on machine learning models.” J. Environ. Manage. 293 (Sep): 112810. https://doi.org/10.1016/j.jenvman.2021.112810.
Chen, W., Y. Li, W. Xue, H. Shahabi, S. Li, H. Hong, X. Wang, H. Bian, S. Zhang, and B. Pradhan. 2020. “Modeling flood susceptibility using data-driven approaches of naïve bayes tree, alternating decision tree, and random forest methods.” Sci. Total Environ 701 (Jan): 134979. https://doi.org/10.1016/j.scitotenv.2019.134979.
Choubin, B., E. Moradi, M. Golshan, J. Adamowski, F. Sajedi-Hosseini, and A. Mosavi. 2019. “An ensemble prediction of flood susceptibility using multivariate discriminant analysis, classification and regression trees, and support vector machines.” Sci. Total Environ 651 (Feb): 2087–2096. https://doi.org/10.1016/j.scitotenv.2018.10.064.
Cox, D. R. 1958. “The regression analysis of binary sequences (with discussion).” J. R. Stat. Soc. B 20 (2): 215–242. https://doi.org/10.1111/j.2517-6161.1958.tb00292.x.
Darabi, H., B. Choubin, O. Rahmati, A. T. Haghighi, B. Pradhan, and B. Kløve. 2019. “Urban flood risk mapping using the GARP and QUEST models: A comparative study of machine learning techniques.” J. Hydrol. 569 (Feb): 142–154. https://doi.org/10.1016/j.jhydrol.2018.12.002.
Darabi, H., A. T. Haghighi, O. Rahmati, A. J. Shahrood, S. Rouzbeh, B. Pradhan, and D. T. Bui. 2021. “A hybridized model based on neural network and swarm intelligence-grey wolf algorithm for spatial prediction of urban flood-inundation.” J. Hydrol. 603 (Dec): 126854. https://doi.org/10.1016/j.jhydrol.2021.126854.
Dodangeh, E., B. Choubin, A. N. Eigdir, N. Nabipour, M. Panahi, S. Shamshirband, and A. Mosavi. 2020. “Integrated machine learning methods with resampling algorithms for flood susceptibility prediction.” Sci. Total Environ. 705 (Feb): 135983. https://doi.org/10.1016/j.scitotenv.2019.135983.
Eastman, J. R. 2015. TerrSet manual. Accessed in TerrSet version. Worcester, MA: Clark Univ.
Eastman, J. R. 2016. TerrSet geospatial monitoring and modeling system. 345–389. Worcester, MA: Clark Univ.
Eastman Ronald, J., A. L. Solarzano, and E. M. Van Fossen. 2005. “Transition potential modeling for land cover change.” In GIS Spatial analysis and modelling, edited by M. J. David, B. Michael, and G. F. Michael. Redlands, CA: Esri.
Eini, M., H. S. Kaboli, M. Rashidian, and H. Hedayat. 2020. “Hazard and vulnerability in urban flood risk mapping: Machine learning techniques and considering the role of urban districts.” Int. J. Disaster Risk Reduct. 50 (Mar): 101687. https://doi.org/10.1016/j.ijdrr.2020.101687.
El-Haddad, B. A., A. M. Youssef, H. R. Pourghasemi, B. Pradhan, A.-H. El-Shater, and M. H. El-Khashab. 2021. “Flood susceptibility prediction using four machine learning techniques and comparison of their performance at Wadi Qena Basin, Egypt.” Nat. Hazards 105 (1): 83–114. https://doi.org/10.1007/s11069-020-04296-y.
Esfandiari, M., S. Jabari, H. McGrath, and D. Coleman. 2020. “Flood mapping using random forest and identifying the essential conditioning factors; a case study in Fredericton, New Brunswick, Canada.” ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci. 3 (Aug): 609–615. https://doi.org/10.5194/isprs-annals-V-3-2020-609-2020.
Fahad, G., R. Nazari, J. Daraio, and D. Lundberg. 2017. “Regional study of future temperature and precipitation changes using Bias corrected multi-model ensemble projections considering high Emission pathways.” J. Earth Sci. Clim. Chang 38 (4): 1634–1648. https://doi.org/10.1002/joc.5284.
Fahad, M. G. R., R. Nazari, M. Motamedi, and M. E. Karimi. 2020. “Coupled hydrodynamic and geospatial model for assessing resiliency of coastal structures under extreme storm scenarios.” Water Resour. Manage. 34 (Feb): 1123–1138. https://doi.org/10.1007/s11269-020-02490-y.
Feng, Y., S. Chen, X. Tong, Z. Lei, C. Gao, and J. Wang. 2020. “Modeling changes in China’s 2000–2030 carbon stock caused by land use change.” J. Cleaner Prod. 252 (May): 119659. https://doi.org/10.1016/j.jclepro.2019.119659.
Gokceoglu, C., H. Sonmez, H. A. Nefeslioglu, T. Y. Duman, and T. Can. 2005. “The 17 March 2005 Kuzulu landslide (Sivas, Turkey) and landslide-susceptibility map of its near vicinity.” Eng. Geol. 81 (1): 65–83. https://doi.org/10.1016/j.enggeo.2005.07.011.
Guo, Y., G. Fang, Y.-P. Xu, X. Tian, and J. Xie. 2021. “Responses of hydropower generation and sustainability to changes in reservoir policy, climate and land use under uncertainty: A case study of Xinanjiang Reservoir in China.” J. Cleaner Prod. 281 (Jan): 124609. https://doi.org/10.1016/j.jclepro.2020.124609.
Hagos, Y. G., T. G. Andualem, M. Yibeltal, and M. A. Mengie. 2022. “Flood hazard assessment and mapping using GIS integrated with multi-criteria decision analysis in upper Awash River basin, Ethiopia.” Appl. Water Sci. 12 (7): 148. https://doi.org/10.1007/s13201-022-01674-8.
Ho, T. K., 1995. “Random decision forests.” In Proc., 3rd Int. Conf. on Document Analysis and Recognition, 278–282. New York: IEEE.
Ho, T. K. 1998. “The random subspace method for constructing decision forests.” IEEE Trans. Pattern Anal. Mach. Intell. 20 (8): 832–844. https://doi.org/10.1109/34.709601.
Hong, H., J. Liu, and A.-X. Zhu. 2020. “Modeling landslide susceptibility using LogitBoost alternating decision trees and forest by penalizing attributes with the bagging ensemble.” Sci. Total Environ. 718 (May): 137231. https://doi.org/10.1016/j.scitotenv.2020.137231.
Hosseini, F. S., B. Choubin, A. Mosavi, N. Nabipour, S. Shamshirband, H. Darabi, and A. T. Haghighi. 2020. “Flash-flood hazard assessment using ensembles and Bayesian-based machine learning models: Application of the simulated annealing feature selection method.” Sci. Total Environ. 711 (Apr): 135161. https://doi.org/10.1016/j.scitotenv.2019.135161.
Ibrahim Mahmoud, M., A. Duker, C. Conrad, M. Thiel, and H. Shaba Ahmad. 2016. “Analysis of settlement expansion and urban growth modelling using geoinformation for assessing potential impacts of urbanization on climate in Abuja City, Nigeria.” Remote Sens. 8 (3): 220. https://doi.org/10.3390/rs8030220.
Kafy, A.-A., A. Al Rakib, K. S. Akter, D. M. A. Jahir, M. S. Sikdar, T. J. Ashrafi, S. Mallik, and M. M. Rahman. 2021. “Assessing and predicting land use/land cover, land surface temperature and urban thermal field variance index using Landsat imagery for Dhaka Metropolitan area.” Environ. Challenges 4 (Mar): 100192. https://doi.org/10.1016/j.envc.2021.100192.
Kefi, M., B. K. Mishra, P. Kumar, Y. Masago, and K. Fukushi. 2018. “Assessment of tangible direct flood damage using a spatial analysis approach under the effects of climate change: Case study in an urban watershed in Hanoi, Vietnam.” ISPRS Int. J. Geo-Inf. 7 (1): 29. https://doi.org/10.3390/ijgi7010029.
Khosravi, K., B. T. Pham, K. Chapi, A. Shirzadi, H. Shahabi, I. Revhaug, I. Prakash, and D. T. Bui. 2018. “A comparative assessment of decision trees algorithms for flash flood susceptibility modeling at Haraz watershed, northern Iran.” Sci. Total Environ. 627 (Jun): 744–755. https://doi.org/10.1016/j.scitotenv.2018.01.266.
Khosravi, K., H. Shahabi, B. T. Pham, J. Adamowski, A. Shirzadi, B. Pradhan, J. Dou, H.-B. Ly, G. Gróf, and H. L. Ho. 2019. “A comparative assessment of flood susceptibility modeling using multi-criteria decision-making analysis and machine learning methods.” J. Hydrol. 573 (Jun): 311–323. https://doi.org/10.1016/j.jhydrol.2019.03.073.
Kim, Y., and G. Newman. 2019. “Climate change preparedness: Comparing future urban growth and flood risk in Amsterdam and Houston.” Sustainability 11 (4): 1048. https://doi.org/10.3390/su11041048.
Landis, J. R., and G. G. Koch. 1977. “The measurement of observer agreement for categorical data.” Biometrics 33 (1): 159–174. https://doi.org/10.2307/2529310.
Leta, M. K., T. A. Demissie, and J. Tränckner. 2021. “Modeling and prediction of land use land cover change dynamics based on land change modeler (LCM) in nashe watershed, upper blue Nile Basin, Ethiopia.” Sustainability 13 (7): 3740. https://doi.org/10.3390/su13073740.
Li, X., D. Yan, K. Wang, B. Weng, T. Qin, and S. Liu. 2019. “Flood risk assessment of global watersheds based on multiple machine learning models.” Water 11 (8): 1654. https://doi.org/10.3390/w11081654.
Liu, D., Z. Fan, Q. Fu, M. Li, M. A. Faiz, S. Ali, T. Li, L. Zhang, and M. I. Khan. 2020. “Random forest regression evaluation model of regional flood disaster resilience based on the whale optimization algorithm.” J. Cleaner Prod. 250 (Mar): 119468. https://doi.org/10.1016/j.jclepro.2019.119468.
Ma, M., C. Liu, G. Zhao, H. Xie, P. Jia, D. Wang, H. Wang, and Y. Hong. 2019. “Flash flood risk analysis based on machine learning techniques in the Yunnan Province, China.” Remote Sens. 11 (2): 170. https://doi.org/10.3390/rs11020170.
Mahmoud, S. H., and T. Y. Gan. 2018. “Multi-criteria approach to develop flood susceptibility maps in arid regions of Middle East.” J. Cleaner Prod. 196 (Sep): 216–229. https://doi.org/10.1016/j.jclepro.2018.06.047.
Marhaento, H., M. J. Booij, and A. Y. Hoekstra. 2018. “Hydrological response to future land-use change and climate change in a tropical catchment.” Hydrol. Sci. J. 63 (9): 1368–1385. https://doi.org/10.1080/02626667.2018.1511054.
McFadden, D. 1973. “Conditional logit analysis of qualitative choice behavior.” In Frontiers in econometrics, edited by P. Zarembka. New York: Academic Press.
Merz, B., A. Thieken, and M. Gocht. 2007. Flood risk mapping at the local scale: Concepts and challenges, Flood risk management in Europe. 231–251. New York: Springer.
Mirakhorlo, M. S., and M. Rahimzadegan. 2021. “Analysing the land-use change effects on soil erosion and sediment in the North of Iran; a case study: Talar watershed.” Geocarto Int. 36 (8): 936–956. https://doi.org/10.1080/10106049.2019.1624985.
Mishra, B. K., A. Rafiei Emam, Y. Masago, P. Kumar, R. K. Regmi, and K. Fukushi. 2018. “Assessment of future flood inundations under climate and land use change scenarios in the Ciliwung River Basin, Jakarta.” J. Flood Risk Manage. 11 (Feb): S1105–S1115. https://doi.org/10.1111/jfr3.12311.
Mojaddadi, H., B. Pradhan, H. Nampak, N. Ahmad, and A. H. B. Ghazali. 2017. “Ensemble machine-learning-based geospatial approach for flood risk assessment using multi-sensor remote-sensing data and GIS.” Geomatics Nat. Hazards Risk 8 (2): 1080–1102. https://doi.org/10.1080/19475705.2017.1294113.
Moore, I. D., R. Grayson, and A. Ladson. 1991. “Digital terrain modelling: A review of hydrological, geomorphological, and biological applications.” Hydrol. Processes 5 (1): 3–30. https://doi.org/10.1002/hyp.3360050103.
Nachappa, T. G., O. Ghorbanzadeh, K. Gholamnia, and T. Blaschke. 2020a. “Multi-hazard exposure mapping using machine learning for the State of Salzburg, Austria.” Remote Sens. 12 (17): 2757. https://doi.org/10.3390/rs12172757.
Nachappa, T. G., S. T. Piralilou, K. Gholamnia, O. Ghorbanzadeh, O. Rahmati, and T. Blaschke. 2020b. “Flood susceptibility mapping with machine learning, multi-criteria decision analysis and ensemble using Dempster Shafer Theory.” J. Hydrol. 590 (Nov): 125275. https://doi.org/10.1016/j.jhydrol.2020.125275.
Nazari, R., H. Vasiliadis, M. Karimi, M. G. R. Fahad, S. Simon, T. Zhang, Q. Sun, and R. Peters. 2022. “Hydrodynamic study of the impact of extreme flooding events on wastewater treatment plants considering total water level.” Nat. Hazards Rev. 23 (1): 04021056. https://doi.org/10.1061/(ASCE)NH.1527-6996.0000531.
Pal, S., S. Kundu, and S. Mahato. 2020. “Groundwater potential zones for sustainable management plans in a river basin of India and Bangladesh.” J. Cleaner Prod. 257 (Jun): 120311. https://doi.org/10.1016/j.jclepro.2020.120311.
Rahman, M., N. Chen, M. M. Islam, G. I. Mahmud, H. R. Pourghasemi, M. Alam, M. A. Rahim, M. A. Baig, A. Bhattacharjee, and A. Dewan. 2021. “Development of flood hazard map and emergency relief operation system using hydrodynamic modeling and machine learning algorithm.” J. Cleaner Prod. 311 (Jun): 127594. https://doi.org/10.1016/j.jclepro.2021.127594.
Rahman, M., C. Ningsheng, M. M. Islam, A. Dewan, J. Iqbal, R. M. A. Washakh, and T. Shufeng. 2019. “Flood susceptibility assessment in Bangladesh using machine learning and multi-criteria decision analysis.” Earth Syst. Environ. 3 (3): 585–601. https://doi.org/10.1007/s41748-019-00123-y.
Rahmati, O., S. Yousefi, Z. Kalantari, E. Uuemaa, T. Teimurian, S. Keesstra, T. D. Pham, and D. Tien Bui. 2019. “Multi-hazard exposure mapping using machine learning techniques: A case study from Iran.” Remote Sens. 11 (16): 1943. https://doi.org/10.3390/rs11161943.
Roy, P., S. C. Pal, R. Chakrabortty, I. Chowdhuri, S. Malik, and B. Das. 2020. “Threats of climate and land use change on future flood susceptibility.” J. Cleaner Prod. 272 (May): 122757. https://doi.org/10.1016/j.jclepro.2020.122757.
Saha, A., S. C. Pal, M. Santosh, S. Janizadeh, I. Chowdhuri, A. Norouzi, P. Roy, and R. Chakrabortty. 2021a. “Modelling multi-hazard threats to cultural heritage sites and environmental sustainability: The present and future scenarios.” J. Cleaner Prod. 320 (May): 128713. https://doi.org/10.1016/j.jclepro.2021.128713.
Saha, S., P. Gogoi, A. Gayen, and G. C. Paul. 2021b. “Constructing the machine learning techniques based spatial drought vulnerability index in Karnataka state of India.” J. Cleaner Prod. 314 (May): 128073. https://doi.org/10.1016/j.jclepro.2021.128073.
Sahoo, S., I. Sil, A. Dhar, A. Debsarkar, P. Das, and A. Kar. 2018. “Future scenarios of land-use suitability modeling for agricultural sustainability in a river basin.” J. Cleaner Prod. 205 (May): 313–328. https://doi.org/10.1016/j.jclepro.2018.09.099.
Sannigrahi, S., Q. Zhang, P. Joshi, P. C. Sutton, S. Keesstra, P. Roy, F. Pilla, B. Basu, Y. Wang, and S. Jha. 2020. “Examining effects of climate change and land use dynamic on biophysical and economic values of ecosystem services of a natural reserve region.” J. Cleaner Prod. 257 (May): 120424. https://doi.org/10.1016/j.jclepro.2020.120424.
Shafapour Tehrany, M., L. Kumar, M. Neamah Jebur, and F. Shabani. 2019. “Evaluating the application of the statistical index method in flood susceptibility mapping and its comparison with frequency ratio and logistic regression methods.” Geomatics Nat. Hazards Risk 10 (1): 79–101. https://doi.org/10.1080/19475705.2018.1506509.
Szwagrzyk, M., D. Kaim, B. Price, A. Wypych, E. Grabska, and J. Kozak. 2018. “Impact of forecasted land use changes on flood risk in the Polish Carpathians.” Nat. Hazards 94 (1): 227–240. https://doi.org/10.1007/s11069-018-3384-y.
Tehrany, M. S., S. Jones, and F. Shabani. 2019. “Identifying the essential flood conditioning factors for flood prone area mapping using machine learning techniques.” Catena 175 (Apr): 174–192. https://doi.org/10.1016/j.catena.2018.12.011.
Tehrany, M. S., and L. Kumar. 2018. “The application of a Dempster–Shafer-based evidential belief function in flood susceptibility mapping and comparison with frequency ratio and logistic regression methods.” Environ. Earth Sci. 77 (13): 1–24. https://doi.org/10.1007/s12665-018-7667-0.
Tehrany, M. S., B. Pradhan, S. Mansor, and N. Ahmad. 2015. “Flood susceptibility assessment using GIS-based support vector machine model with different kernel types.” Catena 125 (Feb): 91–101. https://doi.org/10.1016/j.catena.2014.10.017.
Tellman, B., J. E. Saiers, and O. A. R. Cruz. 2016. “Quantifying the impacts of land use change on flooding in data-poor watersheds in El Salvador with community-based model calibration.” Reg. Environ. Change 16 (4): 1183–1196. https://doi.org/10.1007/s10113-015-0841-y.
USGS. 2022. “Earth explorer: Online search and ordering tool of satellite, aircraft, and other remote sensing inventories.” Accessed August 5, 2023. https://earthexplorer.usgs.gov/.
Varamesh, S., S. Hosseini, and M. Rahimzadegan. 2017. “Detection of land use changes in northeastern Iran by landsat satellite data.” Appl. Ecol. Environ. Res. 15 (3): 1443–1454. https://doi.org/10.15666/aeer/1503_14431454.
Xu, L., S. Cui, X. Wang, J. Tang, V. Nitivattananon, S. Ding, and M. N. Nguyen. 2021. “Dynamic risk of coastal flood and driving factors: Integrating local sea level rise and spatially explicit urban growth.” J. Cleaner Prod. 321 (Oct): 129039. https://doi.org/10.1016/j.jclepro.2021.129039.
Yariyan, P., M. Avand, R. A. Abbaspour, A. Torabi Haghighi, R. Costache, O. Ghorbanzadeh, S. Janizadeh, and T. Blaschke. 2020. “Flood susceptibility mapping using an improved analytic network process with statistical models.” Geomatics Nat. Hazards Risk 11 (1): 2282–2314. https://doi.org/10.1080/19475705.2020.1836036.
Zare, M., T. Panagopoulos, and L. Loures. 2017. “Simulating the impacts of future land use change on soil erosion in the Kasilian watershed, Iran.” Land Use Policy 67 (Sep): 558–572. https://doi.org/10.1016/j.landusepol.2017.06.028.
Zeleňáková, M., R. Fijko, S. Labant, E. Weiss, G. Markovič, and R. Weiss. 2019. “Flood risk modelling of the Slatvinec stream in Kružlov village, Slovakia.” J. Cleaner Prod. 212 (Mar): 109–118. https://doi.org/10.1016/j.jclepro.2018.12.008.
Zhao, G., B. Pang, Z. Xu, D. Peng, and L. Xu. 2019. “Assessment of urban flood susceptibility using semi-supervised machine learning model.” Sci. Total Environ. 659 (Apr): 940–949. https://doi.org/10.1016/j.scitotenv.2018.12.217.
Zope, P., T. Eldho, and V. Jothiprakash. 2016. “Impacts of land use–land cover change and urbanization on flooding: A case study of Oshiwara River Basin in Mumbai, India.” Catena 145 (Oct): 142–154. https://doi.org/10.1016/j.catena.2016.06.009.
Information & Authors
Information
Published In
Natural Hazards Review
Volume 25 • Issue 1 • February 2024
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Jan 22, 2023
Accepted: Aug 8, 2023
Published online: Oct 5, 2023
Published in print: Feb 1, 2024
Discussion open until: Mar 5, 2024
ASCE Technical Topics:
- Algorithms
- Artificial intelligence and machine learning
- Computer programming
- Computing in civil engineering
- Engineering fundamentals
- Floods
- Geographic information systems
- Geomatics
- Infrastructure
- Land use
- Mapping
- Mathematics
- Neural networks
- River engineering
- River systems
- Surveying methods
- Urban and regional development
- Urban areas
- Water and water resources
- Watersheds
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.