Using fuzzy and machine learning iterative optimized models to generate the flood susceptibility maps: case study of Prahova River basin, Romania

References

• Abdulrazzak M, Elfeki A, Kamis A, Kassab M, Alamri N, Chaabani A, Noor K. 2019. Flash flood risk assessment in urban arid environment: case study of Taibah and Islamic universities’ campuses, Medina, Kingdom of Saudi Arabia. Geomatics Nat Hazards Risk. 10(1):780–796. doi: 10.1080/19475705.2018.1545705.
   Web of Science ®Google Scholar
• Ahmadlou M, Al‐Fugara A, Al‐Shabeeb AR, Arora A, Al‐Adamat R, Pham QB, Al‐Ansari N, Linh NTT, Sajedi H. 2021. Flood susceptibility mapping and assessment using a novel deep learning model combining multilayer perceptron and autoencoder neural networks. J Flood Risk Management. 14(1):e12683. doi: 10.1111/jfr3.12683.
   Web of Science ®Google Scholar
• Ahmadlou M, Karimi M, Alizadeh S, Shirzadi A, Parvinnejhad D, Shahabi H, Panahi M. 2019. Flood susceptibility assessment using integration of adaptive network-based fuzzy inference system (ANFIS) and biogeography-based optimization (BBO) and BAT algorithms (BA). Geocarto Int. 34(11):1252–1272. doi: 10.1080/10106049.2018.1474276.
   Web of Science ®Google Scholar
• Akay H. 2021. Flood hazards susceptibility mapping using statistical, fuzzy logic, and MCDM methods. Soft Comput. 25(14):9325–9346. doi: 10.1007/s00500-021-05903-1.
   Web of Science ®Google Scholar
• Ali SA, Parvin F, Pham QB, Vojtek M, Vojteková J, Costache R, Linh NTT, Nguyen HQ, Ahmad A, Ghorbani MA. 2020. GIS-based comparative assessment of flood susceptibility mapping using hybrid multi-criteria decision-making approach, naïve Bayes tree, bivariate statistics and logistic regression: a case of Topľa basin, Slovakia. Ecol Indic. 117:106620. doi: 10.1016/j.ecolind.2020.106620.
   Web of Science ®Google Scholar
• Al-Taani A, Al-Husban Y, Ayan A. 2023. Assessment of potential flash flood hazards. Concerning land use/land cover in Aqaba Governorate, Jordan, using a multi-criteria technique. Egyptian J Remote Sens Space Sci. 26(1):17–24. doi: 10.1016/j.ejrs.2022.12.007.
   Web of Science ®Google Scholar
• Arabameri A, Saha S, Chen W, Roy J, Pradhan B, Bui DT. 2020. Flash flood susceptibility modelling using functional tree and hybrid ensemble techniques. J Hydrol. 587:125007. doi: 10.1016/j.jhydrol.2020.125007.
   Web of Science ®Google Scholar
• Avand M, Kuriqi A, Khazaei M, Ghorbanzadeh O. 2022. DEM resolution effects on machine learning performance for flood probability mapping. J Hydro-Environ Res. 40:1–16. doi: 10.1016/j.jher.2021.10.002.
   Web of Science ®Google Scholar
• Ayyildiz E, Taskin Gumus A. 2021. Pythagorean fuzzy AHP based risk assessment methodology for hazardous material transportation: an application in Istanbul. Environ Sci Pollut Res Int. 28(27):35798–35810. doi: 10.1007/s11356-021-13223-y.
   PubMed Web of Science ®Google Scholar
• Azareh A, Rafiei Sardooi E, Choubin B, Barkhori S, Shahdadi A, Adamowski J, Shamshirband S. 2021. Incorporating multi-criteria decision-making and fuzzy-value functions for flood susceptibility assessment. Geocarto Int. 36(20):2345–2365. doi: 10.1080/10106049.2019.1695958.
   Web of Science ®Google Scholar
• Bui DT, Hoang N-D, Martínez-Álvarez F, Ngo P-TT, Hoa PV, Pham TD, Samui P, Costache R. 2020. A novel deep learning neural network approach for predicting flash flood susceptibility: a case study at a high frequency tropical storm area. Sci Total Environ. 701:134413. doi: 10.1016/j.scitotenv.2019.134413.
   PubMed Web of Science ®Google Scholar
• Cao Y, Jia H, Xiong J, Cheng W, Li K, Pang Q, Yong Z. 2020. Flash flood susceptibility assessment based on geodetector, certainty factor, and logistic regression analyses in Fujian Province, China. IJGI. 9(12):748. doi: 10.3390/ijgi9120748.
   Google Scholar
• Chapi K, Singh VP, Shirzadi A, Shahabi H, Bui DT, Pham BT, Khosravi K. 2017. A novel hybrid artificial intelligence approach for flood susceptibility assessment. Environ Modelling & Software. 95:229–245. doi: 10.1016/j.envsoft.2017.06.012.
   Web of Science ®Google Scholar
• Chen W, Liu W, Liang H, Jiang M, Dai Z. 2023. Response of storm surge and M2 tide to typhoon speeds along coastal Zhejiang Province. Ocean Eng. 270:113646.doi: 10.1016/j.oceaneng.2023.113646.
   Web of Science ®Google Scholar
• Chen W, Li Y, Xue W, Shahabi H, Li S, Hong H, Wang X, Bian H, Zhang S, Pradhan B, et al. 2020. Modeling flood susceptibility using data-driven approaches of naïve bayes tree, alternating decision tree, and random forest methods. Sci Total Environ. 701:134979., doi: 10.1016/j.scitotenv.2019.134979.
   PubMed Web of Science ®Google Scholar
• Chowdhuri I, Pal SC, Chakrabortty R. 2020. Flood susceptibility mapping by ensemble evidential belief function and binomial logistic regression model on river basin of eastern India. Adv Space Res. 65(5):1466–1489. doi: 10.1016/j.asr.2019.12.003.
   Web of Science ®Google Scholar
• Costache R, Bui DT. 2019. Spatial prediction of flood potential using new ensembles of bivariate statistics and artificial intelligence: a case study at the Putna river catchment of Romania. Sci Total Environ. 691:1098–1118. doi: 10.1016/j.scitotenv.2019.07.197.
   PubMed Web of Science ®Google Scholar
• Costache R, Trung Tin T, Arabameri A, Crăciun A, Ajin RS, Costache I, Reza Md. Towfiqul Islam A, Abba SI, Sahana M, Avand M, et al. 2022. Flash-flood hazard using deep learning based on H2O R package and fuzzy-multicriteria decision-making analysis. J Hydrol. 609:127747. doi: 10.1016/j.jhydrol.2022.127747.
   Web of Science ®Google Scholar
• Costache R. 2019. Flood susceptibility assessment by using bivariate statistics and machine learning models-A useful tool for flood risk management. Water Resour Manage. 33(9):3239–3256. doi: 10.1007/s11269-019-02301-z.
   Web of Science ®Google Scholar
• Dahri N, Abida H. 2017. Monte Carlo simulation-aided analytical hierarchy process (AHP) for flood susceptibility mapping in Gabes Basin (southeastern Tunisia). Environ Earth Sci. 76(7):302. doi: 10.1007/s12665-017-6619-4.
   Web of Science ®Google Scholar
• Fang Z, Wang Y, Peng L, Hong H. 2021. A comparative study of heterogeneous ensemble-learning techniques for landslide susceptibility mapping. Int J Geographical Information Sci. 35(2):321–347. doi: 10.1080/13658816.2020.1808897.
   Web of Science ®Google Scholar
• Fekete A. 2019. Critical infrastructure and flood resilience: cascading effects beyond water. Wiley Interdiscip Rev: Water. 6:e1370.
   Web of Science ®Google Scholar
• Gao C, Hao M, Chen J, Gu C. 2021. Simulation and design of joint distribution of rainfall and tide level in Wuchengxiyu Region,China. Urban Clim. 40:101005.doi: 10.1016/j.uclim.2021.101005.
   Web of Science ®Google Scholar
• Golkarian A, Khosravi K, Panahi M, Clague JJ. 2023. Spatial variability of soil water erosion: comparing empirical and intelligent techniques. Geosci Front. 14(1):101456. doi: 10.1016/j.gsf.2022.101456.
   Web of Science ®Google Scholar
• Handini DR, Hidayah E, Halik G. 2021. Flash flood susceptibility mapping at Andungbiru Watershed, East Java using AHP-information weighted method. Geos Ind. 6(2):127–142. doi: 10.19184/geosi.v6i2.24173.
   Google Scholar
• Ho M, Nathan R, Wasko C, Vogel E, Sharma A. 2022. Projecting changes in flood event runoff coefficients under climate change. J Hydrol. 615:128689. doi: 10.1016/j.jhydrol.2022.128689.
   Web of Science ®Google Scholar
• Holmes G, Pfahringer B, Kirkby R, Frank E, Hall M. 2002. Multiclass alternating decision trees. Presented at the Machine Learning: ECML 2002: 13th European Conference on Machine Learning; August 19–23, 2002; Helsinki, Finland. Proceedings 13, Springer. p. 161–172.
   Google Scholar
• Huang F, Cao Z, Guo J, Jiang S-H, Li S, Guo Z. 2020. Comparisons of heuristic, general statistical and machine learning models for landslide susceptibility prediction and mapping. Catena. 191:104580. doi: 10.1016/j.catena.2020.104580.
   Web of Science ®Google Scholar
• Islam ARMT, Talukdar S, Mahato S, Kundu S, Eibek KU, Pham QB, Kuriqi A, Linh, NTT. 2021. Flood susceptibility modelling using advanced ensemble machine learning models. Geosci Front. 12(3):101075. doi: 10.1016/j.gsf.2020.09.006.
   Web of Science ®Google Scholar
• Jiroušek R, Shenoy PP. 2016. Entropy of belief functions in the Dempster-Shafer theory: a new perspective. Presented at the Belief Functions: Theory and Applications: 4th International Conference, BELIEF 2016; September 21–23, 2016; Prague, Czech Republic. Proceedings, Springer. p. 3–13.
   Google Scholar
• Khosravi K, Barzegar R, Golkarian A, Busico G, Cuoco E, Mastrocicco M, Colombani N, Tedesco D, Ntona MM, Kazakis N. 2021. Predictive modeling of selected trace elements in groundwater using hybrid algorithms of iterative classifier optimizer. J Contam Hydrol. 242:103849. doi: 10.1016/j.jconhyd.2021.103849.
   PubMed Web of Science ®Google Scholar
• Kuriqi A, Ardiçlioǧlu M. 2018. Investigation of hydraulic regime at middle part of the Loire River in context of floods and low flow events. Pollack Periodica. 13(1):145–156. doi: 10.1556/606.2018.13.1.13.
   Google Scholar
• Kuriqi A, Hysa A. 2021. Multidimensional aspects of floods: nature-based mitigation measures from basin to river reach scale. In: Nature-based solutions for flood mitigation: environmental and socio-economic aspects. Charm: Springer International Publishing. p. 11–33.
   Google Scholar
• Lee S, Kim J-C, Jung H-S, Lee MJ, Lee S. 2017. Spatial prediction of flood susceptibility using random-forest and boosted-tree models in Seoul metropolitan city, Korea. Geomatics Nat Hazards Risk. 8(2):1185–1203. doi: 10.1080/19475705.2017.1308971.
   Web of Science ®Google Scholar
• Li W, Zhu J, Fu L, Zhu Q, Xie Y, Hu Y. 2021. An augmented representation method of debris flow scenes to improve public perception. Int J Geogr Inf Sci. 35(8):1521–1544. doi: 10.1080/13658816.2020.1833016.
   Web of Science ®Google Scholar
• Li R, Zhang H, Chen Z, Yu N, Kong W, Li T, Wang E, Wu X, Liu Y. 2022. Denoising method of ground-penetrating radar signal based on independent component analysis with multifractal spectrum. Measurement. 192:110886.doi: 10.1016/j.measurement.2022.110886.
   Web of Science ®Google Scholar
• Li Q, Song D, Yuan C, Nie W. 2022. An image recognition method for the deformation area of open-pit rock slopes under variable rainfall. Measurement. 188:110544.doi: 10.1016/j.measurement.2021.110544.
   Web of Science ®Google Scholar
• Li H, Lei X, Shang Y, Qin T. 2018. Flash flood early warning research in China. Int J Water Resour Dev. 34(3):369–385. doi: 10.1080/07900627.2018.1435409.
   Web of Science ®Google Scholar
• Li Y, Hong H. 2023. Modelling flood susceptibility based on deep learning coupling with ensemble learning models. J Environ Manage. 325(Pt A):116450. doi: 10.1016/j.jenvman.2022.116450.
   PubMedGoogle Scholar
• Luo J, Wang G, Li G, Pesce G. 2022a. Transport infrastructure connectivity and conflict resolution: a machine learning analysis. Neural Comput Applic. 34(9):6585–6601. doi: 10.1007/s00521-021-06015-5.
   Web of Science ®Google Scholar
• Luo J, Niu F, Lin Z, Liu M, Yin G, Gao Z. 2022b. Abrupt increase in thermokarst lakes on the central Tibetan Plateau over the last 50 years. CATENA. 217:106497.doi: 10.1016/j.catena.2022.106497.
   Web of Science ®Google Scholar
• Luu C, Pham BT, Phong TV, Costache R, Nguyen HD, Amiri M, Bui QD, Nguyen LT, Le HV, Prakash I, et al. 2021. GIS-based ensemble computational models for flood susceptibility prediction in the Quang Binh Province, Vietnam. J Hydrol. 599:126500. doi: 10.1016/j.jhydrol.2021.126500.
   Web of Science ®Google Scholar
• Ma S, Qiu H, Yang D, Wang J, Zhu Y, Tang B, Sun K, Cao M. 2023. Surface multi-hazard effect of underground coal mining. Landslides. 20(1):39–52. doi: 10.1007/s10346-022-01961-0.
   Web of Science ®Google Scholar
• Marino D, Palmieri M, Marucci A, Soraci M, Barone A, Pili S. 2023. Linking flood risk mitigation and food security: an analysis of land-use change in the metropolitan area of Rome. Land. 12(2):366. doi: 10.3390/land12020366.
   Web of Science ®Google Scholar
• Mi C, Liu Y, Zhang Y, Wang J, Feng Y, Zhang Z. 2023. A vision-based displacement measurement system for foundation pit. IEEE Trans Instrum Meas. 72:1–15. doi: 10.1109/TIM.2023.3311069.
   PubMed Web of Science ®Google Scholar
• Parizi E, Khojeh S, Hosseini SM, Moghadam YJ. 2022. Application of unmanned aerial vehicle DEM in flood modeling and comparison with global DEMs: case study of Atrak River Basin, Iran. J Environ Manage. 317:115492. doi: 10.1016/j.jenvman.2022.115492.
   PubMed Web of Science ®Google Scholar
• Popa MC, Peptenatu D, Drăghici CC, Diaconu DC. 2019. Flood hazard mapping using the flood and flash-flood potential index in the Buzău River catchment, Romania. Water. 11(10):2116. doi: 10.3390/w11102116.
   Web of Science ®Google Scholar
• Rojas O, Soto E, Rojas C, López JJ. 2022. Assessment of the flood mitigation ecosystem service in a coastal wetland and potential impact of future urban development in Chile. Habitat Int. 123:102554. doi: 10.1016/j.habitatint.2022.102554.
   Web of Science ®Google Scholar
• Rui S, Zhou Z, Jostad HP, Wang L, Guo Z. 2023. Numerical prediction of potential 3-dimensional seabed trench profiles considering complex motions of mooring line. Appl Ocean Res. 139:103704.doi: 10.1016/j.apor.2023.103704.
   Web of Science ®Google Scholar
• Saad IA. 2018. An efficient classification algorithms for image retrieval based color and texture features. J Al-Qadisiyah for Computer Sci Mathe. 1042.
   Google Scholar
• Saleh A, Yuzir A, Sabtu N, Abujayyab SK, Bunmi MR, Pham QB. 2022. Flash flood susceptibility mapping in urban area using genetic algorithm and ensemble method. Geocarto Int. 37(25):10199–10228. doi: 10.1080/10106049.2022.2032394.
   Web of Science ®Google Scholar
• Stewart D, Canfield E, Hawkins R. 2012. Curve number determination methods and uncertainty in hydrologic soil groups from semiarid watershed data. J Hydrol Eng. 17(11):1180–1187. doi: 10.1061/(ASCE)HE.1943-5584.0000452.
   Web of Science ®Google Scholar
• Sun X, Chen Z, Guo K, Fei J, Dong Z, Xiong H. 2023. Geopolymeric flocculation-solidification of tail slurry of shield tunnelling spoil after sand separation. Constr Build Mater. 374:130954.doi: 10.1016/j.conbuildmat.2023.130954.
   Web of Science ®Google Scholar
• Tariq A, Beni LH, Ali S, Adnan S, Hatamleh WA. 2023. An effective geospatial-based flash flood susceptibility assessment with hydrogeomorphic responses on groundwater recharge. Groundwater Sustainable Dev. 23:100998. doi: 10.1016/j.gsd.2023.100998.
   Google Scholar
• Tian H, Pei J, Huang J, Li X, Wang J, Zhou B, Qin Y, Wang L. 2020. Garlic and winter wheat identification based on active and passive satellite imagery and the google earth engine in northern china. Remote Sens. 12(21):3539.doi: 10.3390/rs12213539.
   Google Scholar
• Tian H, Huang N, Niu Z, Qin Y, Pei J, Wang J. 2019. Mapping winter crops in china with multi-source satellite imagery and phenology-based algorithm. Remote Sens. 11(7):820.doi: 10.3390/rs11070820.
   Google Scholar
• Tripathi AK, Agrawal S, Gupta RD. 2022. Comparison of GIS-based AHP and fuzzy AHP methods for hospital site selection: a case study for Prayagraj City, India. GeoJournal. 87(5):3507–3528. doi: 10.1007/s10708-021-10445-y.
   PubMed Web of Science ®Google Scholar
• Tuncer T, Dogan S, Ozyurt F. 2020. An automated residual exemplar local binary pattern and iterative ReliefF based COVID-19 detection method using chest X-ray image. Chemometr Intell Lab Syst. 203:104054. doi: 10.1016/j.chemolab.2020.104054.
   PubMed Web of Science ®Google Scholar
• Uddin K, Matin MA. 2021. Potential flood hazard zonation and flood shelter suitability mapping for disaster risk mitigation in Bangladesh using geospatial technology. Progress in Disaster Sci. 11:100185. doi: 10.1016/j.pdisas.2021.100185.
   Google Scholar
• Venegas-Cordero N, Cherrat C, Kundzewicz ZW, Singh J, Piniewski M. 2023. Model-based assessment of flood generation mechanisms over Poland: the roles of precipitation, snowmelt, and soil moisture excess. Sci Total Environ. 891:164626. doi: 10.1016/j.scitotenv.2023.164626.
   PubMed Web of Science ®Google Scholar
• Wang Q, Guo Y, Li W, He J, Wu Z. 2019. Predictive modeling of landslide hazards in Wen County, northwestern China based on information value, weights-of-evidence, and certainty factor. Geomatics Nat Hazards Risk. 10(1):820–835. doi: 10.1080/19475705.2018.1549111.
   Web of Science ®Google Scholar
• Wu X, Feng X, Wang Z, Chen Y, Deng Z. 2023. Multi-source precipitation products assessment on drought monitoring across global major river basins. Atmos Res. 295:106982.doi: 10.1016/j.atmosres.2023.106982.
   Web of Science ®Google Scholar
• Xie X, Xie B, Cheng J, Chu Q, Dooling T. 2021. A simple Monte Carlo method for estimating the chance of a cyclone impact. Nat Hazards. 107(3):2573–2582. doi: 10.1007/s11069-021-04505-2.
   Web of Science ®Google Scholar
• Xu J, Lan W, Ren C, Zhou X, Wang S, Yuan J. 2021. Modeling of coupled transfer of water, heat and solute in saline loess considering sodium sulfate crystallization. Cold Reg Sci Technol. 189:103335. doi:10.1016/j.coldregions.2021.103335.
   Web of Science ®Google Scholar
• Xu Z, Li X, Li J, Xue Y, Jiang S, Liu L, Luo Q, Wu K, Zhang N, Feng Y, et al. 2022. Characteristics of source rocks and genetic origins of natural gas in deep formations, gudian depression, songliao basin, NE China. ACS Earth Space Chem. 6(7):1750–1771. doi: 10.1021/acsearthspacechem.2c00065.
   Google Scholar
• Yang M, Wang H, Hu K, Yin G, Wei Z. 2022. IA-Net$:$ An inception–attention-module-based network for classifying underwater images from others. IEEE J Oceanic Eng. 47(3):704–717. doi: 10.1109/JOE.2021.3126090.
   Web of Science ®Google Scholar
• Yang W, Xu K, Lian J, Ma C, Bin L. 2018. Integrated flood vulnerability assessment approach based on TOPSIS and Shannon entropy methods. Ecol Indic. 89:269–280. doi: 10.1016/j.ecolind.2018.02.015.
   Web of Science ®Google Scholar
• Yao J, Zhang X, Luo W, Liu C, Ren L. 2022. Applications of Stacking/Blending ensemble learning approaches for evaluating flash flood susceptibility. Int J Appl Earth Obs Geoinf. 112:102932. doi: 10.1016/j.jag.2022.102932.
   Web of Science ®Google Scholar
• Yin L, Wang L, Li T, Lu S, Tian J, Yin Z, Li X, Zheng W. 2023a. U-Net-LSTM: Time series-enhanced lake boundary prediction model. Land. 12(10):1859.doi: 10.3390/land12101859.
   Web of Science ®Google Scholar
• Yin L, Wang L, Li J, Lu S, Tian J, Yin Z, Liu S, Zheng W. 2023b. YOLOV4_CSPBi: Enhanced land target detection model. Land. 12(9):1813.doi: 10.3390/land12091813.
   Web of Science ®Google Scholar
• Yin H, Zhang G, Wu Q, Yin S, Soltanian MR, Thanh HV, Dai Z. 2023a. A deep learning-based data-driven approach for predicting mining water inrush from coal seam floor using microseismic monitoring data. IEEE Trans Geosci Remote Sens. 61:1–15. doi: 10.1109/TGRS.2023.3300012.
   Web of Science ®Google Scholar
• Yin H, Wu Q, Yin S, Dong S, Dai Z, Soltanian MR. 2023b. Predicting mine water inrush accidents based on water level anomalies of borehole groups using long short-term memory and isolation forest. J. Hydrol. 616:128813.doi: 10.1016/j.jhydrol.2022.128813.
   Web of Science ®Google Scholar
• Yin L, Wang L, Li T, Lu S, Yin Z, Liu X, Li X, Zheng W. 2023c. U-Net-STN: A novel end-to-end lake boundary prediction model. Land. 12(8):1602.doi: 10.3390/land12081602.
   Web of Science ®Google Scholar
• Zhao M, Zhou Y, Li X, Cheng W, Zhou C, Ma T, Li M, Huang K. 2020. Mapping urban dynamics (1992–2018) in Southeast Asia using consistent nighttime light data from DMSP and VIIRS. Remote Sens Environ 248:111980.doi: 10.1016/j.rse.2020.111980.
   Web of Science ®Google Scholar
• Zhao H, Gu T, Tang J, Gong Z, Zhao P. 2023. Urban flood risk differentiation under land use scenario simulation. Iscience. 26(4):106479. doi: 10.1016/j.isci.2023.106479.
   PubMed Web of Science ®Google Scholar
• Zhao Q, Chen W, Peng C, Wang D, Xue W, Bian H. 2022. Modeling landslide susceptibility using an evidential belief function-based multiclass alternating decision tree and logistic model tree. Environ Earth Sci. 81(15):404. doi: 10.1007/s12665-022-10525-3.
   Web of Science ®Google Scholar
• Zhou G, Zhang R, Huang S. 2021a. Generalized buffering algorithm. IEEE Access. 9:27140–27157. doi: 10.1109/ACCESS.2021.3057719.
   Web of Science ®Google Scholar
• Zhou G, Deng R, Zhou X, Long S, Li W, Lin G, Li X. 2021b. Gaussian inflection point selection for liDAR hidden echo signal decomposition. IEEE Geosci Remote Sens Lett. 19:1–5. doi: 10.1109/LGRS.2021.3107438.
   Web of Science ®Google Scholar
• Zhou G, Li W, Zhou X, Tan Y, Lin G, Li X, Deng R. 2021c. An innovative echo detection system with STM32 gated and PMT adjustable gain for airborne LiDAR. Int J Remote Sens. 42(24):9187–9211. doi: 10.1080/01431161.2021.1975844.
   Web of Science ®Google Scholar
• Zhu W, Chen J, Sun Q, Li Z, Tan W, Wei Y. 2022. Reconstructing of high-spatial-resolution three-dimensional electron density by ingesting SAR-derived VTEC into IRI model. IEEE Geosci Remote Sens Lett. 19:1–5. doi: 10.1109/LGRS.2022.3178242.
   Web of Science ®Google Scholar
• Zhuo Z, Du L, Lu X, Chen J, Cao Z. 2022. Smoothed Lv distribution based three-dimensional imaging for spinning space debris. IEEE Trans Geosci Remote Sens. 60:1–13. doi: 10.1109/TGRS.2022.3174677.
   Web of Science ®Google Scholar