449
Views
5
CrossRef citations to date
0
Altmetric
Research Article

A machine learning approach for spatiotemporal imputation of MODIS chlorophyll-a

ORCID Icon, ORCID Icon, ORCID Icon & ORCID Icon
Pages 7381-7404 | Received 02 Feb 2021, Accepted 15 Jul 2021, Published online: 12 Sep 2021

References

  • Alvera-Azcárate, A., A. Barth, M. Rixen, and J.-M. Beckers. 2005. “Reconstruction of Incomplete Oceanographic Data Sets Using Empirical Orthogonal Functions: Application to the Adriatic Sea Surface Temperature.” Ocean Modelling 9 (4): 325–346. doi:10.1016/j.ocemod.2004.08.001.
  • Aqil, M., I. Kita, A. Yano, and S. Nishiyama. 2007. “A Comparative Study of Artificial Neural Networks and Neuro-fuzzy in Continuous Modeling of the Daily and Hourly Behaviour of Runoff.” Journal of Hydrology 337 (1–2): 22–34. doi:10.1016/j.jhydrol.2007.01.013.
  • Ashouri, H., K.-L. Hsu, S. Sorooshian, D. K. Braithwaite, K. R. Knapp, L. Dewayne Cecil, B. R. Nelson, and O. P. Prat. 2015. “PERSIANN-CDR: Daily Precipitation Climate Data Record from Multisatellite Observations for Hydrological and Climate Studies.” Bulletin of the American Meteorological Society 96 (1): 69–83. doi:10.1175/BAMS-D-13-00068.1.
  • Badrzadeh, H., R. Sarukkalige, and A. W. Jayawardena. 2013. “Impact of Multi-resolution Analysis of Artificial Intelligence Models Inputs on Multi-step Ahead River Flow Forecasting.” Journal of Hydrology 507: 75–85. doi:10.1016/j.jhydrol.2013.10.017.
  • Barth, A., A. Alvera-Azcárate, M. Licer, and J.-M. Beckers. 2020. “DINCAE 1.0: A Convolutional Neural Network with Error Estimates to Reconstruct Sea Surface Temperature Satellite Observations.” Geoscientific Model Development 13 (3): 1609–1622. doi:10.5194/gmd-13-1609-2020.
  • Basak, D., S. Pal, and D. C. Patranabis. 2007. “Support Vector Regression.” Neural Information Processing-Letters and Reviews 11 (10): 203–224.
  • Beckers, J.-M., A. Barth, and A.-A. Aïda. 2006. “DINEOF Reconstruction of Clouded Images Including Error Maps? Application to the Sea-Surface Temperature around Corsican Island.”
  • Beckers, J.-M., and M. Rixen. 2003. “EOF Calculations and Data Filling from Incomplete Oceanographic Datasets.” Journal of Atmospheric and Oceanic Technology 20 (12): 1839–1856. doi:10.1175/1520-0426(2003)020<1839:ECADFF>2.0.CO;2.
  • Belgiu, M., and D. Lucian. 2016. “Random Forest in Remote Sensing: A Review of Applications and Future Directions.” ISPRS Journal of Photogrammetry and Remote Sensing 114: 24–31. doi:10.1016/j.isprsjprs.2016.01.011.
  • Bierman, P., M. Lewis, B. Ostendorf, and J. Tanner. 2011. “A Review of Methods for Analysing Spatial and Temporal Patterns in Coastal Water Quality.” Ecological Indicators 11 (1): 103–114. doi:10.1016/j.ecolind.2009.11.001.
  • Blondeau-Patissier, D., J. F. R. Gower, A. G. Dekker, S. R. Phinn, and V. E. Brando. 2014. “A Review of Ocean Color Remote Sensing Methods and Statistical Techniques for the Detection, Mapping and Analysis of Phytoplankton Blooms in Coastal and Open Oceans.” Progress in Oceanography 123: 123–144.
  • Breiman, L. 2001. “Random Forests.” Machine Learning 45 (1): 5–32. doi:10.1023/A:1010933404324.
  • Caruana, R., and A. Niculescu-Mizil. 2006. “An Empirical Comparison of Supervised Learning Algorithms.” Paper presented at the Proceedings of the 23rd international conference on Machine learning.  Pittsburgh, PA.
  • Chakraborty, K., A. Gupta, A. A. Lotliker, and G. Tilstone. 2016. “Evaluation of Model Simulated and MODIS-Aqua Retrieved Sea Surface Chlorophyll in the Eastern Arabian Sea.” Estuarine, Coastal and Shelf Science 181: 61–69. doi:10.1016/j.ecss.2016.08.002.
  • Chen, S., H. Chuanmin, B. B. Barnes, Y. Xie, G. Lin, and Z. Qiu. 2019. “Improving Ocean Color Data Coverage through Machine Learning.” Remote Sensing of Environment 222: 286–302. doi:10.1016/j.rse.2018.12.023.
  • Cullen, J. J. 1982. “The Deep Chlorophyll Maximum: Comparing Vertical Profiles of Chlorophyll A.” Canadian Journal of Fisheries and Aquatic Sciences 39 (5): 791–803. doi:10.1139/f82-108.
  • Damaševičius, R. 2010. “Optimization of SVM Parameters for Recognition of Regulatory DNA Sequences.” Top 18 (2): 339–353. doi:10.1007/s11750-010-0152-x.
  • Devak, M., C. T. Dhanya, and A. K. Gosain. 2015. “Dynamic Coupling of Support Vector Machine and K-nearest Neighbour for Downscaling Daily Rainfall.” Journal of Hydrology 525: 286–301. doi:10.1016/j.jhydrol.2015.03.051.
  • Dumont, H. J. 1998. “The Caspian Lake: History, Biota, Structure, and Function.” Limnology and Oceanography 43 (1): 44–52. doi:10.4319/lo.1998.43.1.0044.
  • Farjami, H., and A. R. E. Hesari. 2020. “Assessment of Sea Surface Wind Field Pattern over the Caspian Sea Using EOF Analysis.” Regional Studies in Marine Science 35: 101254. doi:10.1016/j.rsma.2020.101254.
  • Feng, L., G. Nowak, T. J. O’Neill, and A. H. Welsh. 2014. “CUTOFF: A Spatio-temporal Imputation Method.” Journal of Hydrology 519: 3591–3605. doi:10.1016/j.jhydrol.2014.11.012.
  • Frolov, S., R. M. Kudela, and J. G. Bellingham. 2013. “Monitoring of Harmful Algal Blooms in the Era of Diminishing Resources: A Case Study of the US West Coast.” Harmful Algae 21: 1–12. doi:10.1016/j.hal.2012.11.001.
  • Fu, Y., X. Shiguo, C. Zhang, and Y. Sun. 2018. “Spatial Downscaling of MODIS Chlorophyll-a Using Landsat 8 Images for Complex Coastal Water Monitoring.” Estuarine, Coastal and Shelf Science 209: 149–159. doi:10.1016/j.ecss.2018.05.031.
  • García, M., D. Riaño, E. Chuvieco, J. Salas, and F. Mark Danson. 2011. “Multispectral and LiDAR Data Fusion for Fuel Type Mapping Using Support Vector Machine and Decision Rules.” Remote Sensing of Environment 115 (6): 1369–1379. doi:10.1016/j.rse.2011.01.017.
  • García, M., S. Saatchi, A. Casas, A. Koltunov, S. L. Ustin, C. Ramirez, and H. Balzter. 2017. “Extrapolating Forest Canopy Fuel Properties in the California Rim Fire by Combining Airborne LiDAR and Landsat OLI Data.” Remote Sensing 9 (4): 394. doi:10.3390/rs9040394.
  • Geiß, C., P. A. Pelizari, L. Blickensdörfer, and H. Taubenböck. 2019. “Virtual Support Vector Machines with Self-learning Strategy for Classification of Multispectral Remote Sensing Imagery.” ISPRS Journal of Photogrammetry and Remote Sensing 151: 42–58. doi:10.1016/j.isprsjprs.2019.03.001.
  • Ghosh, S. 2010. “SVM‐PGSL Coupled Approach for Statistical Downscaling to Predict Rainfall from GCM Output.” Journal of Geophysical Research: Atmospheres 115 (D22): D22. doi:10.1029/2009JD013548.
  • Gizaw, M. S., and T. Y. Gan. 2016. “Regional Flood Frequency Analysis Using Support Vector Regression under Historical and Future Climate.” Journal of Hydrology 538: 387–398. doi:10.1016/j.jhydrol.2016.04.041.
  • Goetz, S. J., N. Gardiner, and J. H. Viers. 2008. “Monitoring Freshwater, Estuarine and Near-shore Benthic Ecosystems with Multi-sensor Remote Sensing: An Introduction to the Special Issue.” Remote Sensing of Environment 112 (11): 3993–3995. doi:10.1016/j.rse.2008.05.016.
  • Gordon, H. R., D. K. Clark, J. W. Brown, O. B. Brown, R. H. Evans, and W. W. Broenkow. 1983. “Phytoplankton Pigment Concentrations in the Middle Atlantic Bight: Comparison of Ship Determinations and CZCS Estimates.” Applied Optics 22 (1): 20–36. doi:10.1364/AO.22.000020.
  • Goyal, M. K., B. Bharti, J. Quilty, J. Adamowski, and A. Pandey. 2014. “Modeling of Daily Pan Evaporation in Sub Tropical Climates Using ANN, LS-SVR, Fuzzy Logic, and ANFIS.” Expert Systems with Applications 41 (11): 5267–5276. doi:10.1016/j.eswa.2014.02.047.
  • Gross, L., S. Thiria, and R. Frouin. 1999. “Applying Artificial Neural Network Methodology to Ocean Color Remote Sensing.” Ecological Modelling 120 (2–3): 237–246. doi:10.1016/S0304-3800(99)00105-2.
  • Hu, C., Z. Lee, and B. Franz. 2012. “Chlorophyll a Algorithms for Oligotrophic Oceans: A Novel Approach Based on Three-band Reflectance Difference.” Journal of Geophysical Research: Oceans 117 (C1): C1. doi:10.1029/2011JC007395.
  • Jamshidi, S., and N. Bakar. 2011. “A Study on Distribution of Chlorophyll-A in the Coastal Waters of Anzali Port, South Caspian Sea.” Ocean Science Discussions 8: 1.
  • Janik, M., P. Bossew, and O. Kurihara. 2018. “Machine Learning Methods as a Tool to Analyse Incomplete or Irregularly Sampled Radon Time Series Data.” Science of the Total Environment 630: 1155–1167. doi:10.1016/j.scitotenv.2018.02.233.
  • Jayaram, C., N. Priyadarshi, J. P. Kumar, T. V. S. U. Bhaskar, D. Raju, and A. J. Kochuparampil. 2018. “Analysis of Gap-free Chlorophyll-a Data from MODIS in Arabian Sea, Reconstructed Using DINEOF.” International Journal of Remote Sensing 39 (21): 7506–7522. doi:10.1080/01431161.2018.1471540.
  • Jerez, J. M., I. Molina, P. J. García-Laencina, E. Alba, N. Ribelles, M. Martín, and L. Franco. 2010. “Missing Data Imputation Using Statistical and Machine Learning Methods in a Real Breast Cancer Problem.” Artificial Intelligence in Medicine 50 (2): 105–115. doi:10.1016/j.artmed.2010.05.002.
  • Kajiyama, T., D. D’Alimonte, and J. C. Cunha. 2011. “Performance Prediction of Ocean Color Monte Carlo Simulations Using Multi-layer Perceptron Neural Networks.” Procedia Computer Science 4: 2186–2195. doi:10.1016/j.procs.2011.04.239.
  • Kashani, M. H., M. A. Ghorbani, Y. Dinpashoh, and S. Shahmorad. 2016. “Integration of Volterra Model with Artificial Neural Networks for Rainfall-runoff Simulation in Forested Catchment of Northern Iran.” Journal of Hydrology 540: 340–354. doi:10.1016/j.jhydrol.2016.06.028.
  • Kenda, K., F. Koprivec, and M. Dunja. 2018. “Optimal Missing Value Estimation Algorithm for Groundwater Levels.” Paper presented at the Multidisciplinary Digital Publishing Institute Proceedings.
  • Kim, J.-W., and Y. A. Pachepsky. 2010. “Reconstructing Missing Daily Precipitation Data Using Regression Trees and Artificial Neural Networks for SWAT Streamflow Simulation.” Journal of Hydrology 394 (3–4): 305–314. doi:10.1016/j.jhydrol.2010.09.005.
  • Kisi, O., and K. S. Parmar. 2016. “Application of Least Square Support Vector Machine and Multivariate Adaptive Regression Spline Models in Long Term Prediction of River Water Pollution.” Journal of Hydrology 534: 104–112. doi:10.1016/j.jhydrol.2015.12.014.
  • Klige, R. K., and M. S. Myagkov. 1992. “Changes in the Water Regime of the Caspian Sea.” Geojournal 27 (3): 299–307. doi:10.1007/BF02482671.
  • Kosarev A.N. 2005. “Physico-Geographical Conditions of the Caspian Sea.” The Caspian Sea Environment, by Kostianoy A.G., Kosarev A.N. The Handbook of Environmental Chemistry, vol 5P. Springer, Berlin, Heidelberg . https://doi.org/10.1007/698_5_002
  • Kostianoy, A. G., and A. N. Kosarev. 2005. The Caspian Sea Environment. Vol. 5. Berlin, Heidelberg: Springer Science & Business Media.
  • Kumar, D., A. Pandey, N. Sharma, and F. Wolfgang-Albert. 2016. “Daily Suspended Sediment Simulation Using Machine Learning Approach.” Catena 138: 77–90. doi:10.1016/j.catena.2015.11.013.
  • Kutser, T. 2009. “Passive Optical Remote Sensing of Cyanobacteria and Other Intense Phytoplankton Blooms in Coastal and Inland Waters.” International Journal of Remote Sensing 30 (17): 4401–4425. doi:10.1080/01431160802562305.
  • Li, L., M. Franklin, M. Girguis, F. Lurmann, J. Wu, N. Pavlovic, C. Breton, F. Gilliland, and R. Habre. 2020. “Spatiotemporal Imputation of MAIAC AOD Using Deep Learning with Downscaling.” Remote Sensing of Environment 237: 111584. doi:10.1016/j.rse.2019.111584.
  • Li, X., J. Sha, and Z.-L. Wang. 2017. “Chlorophyll-a Prediction of Lakes with Different Water Quality Patterns in China Based on Hybrid Neural Networks.” Water 9 (7): 524. doi:10.3390/w9070524.
  • Liaw, A., and M. Wiener. 2002. “Classification and Regression by randomForest.” R News 2 (3): 18–22.
  • Liu, D., and Y. Wang. 2013. “Trends of Satellite Derived Chlorophyll-a (1997–2011) in the Bohai and Yellow Seas, China: Effects of Bathymetry on Seasonal and Inter-annual Patterns.” Progress in Oceanography 116: 154–166. doi:10.1016/j.pocean.2013.07.003.
  • Liu, X., X. Zhu, Q. Zhang, T. Yang, Y. Pan, and P. Sun. 2020. “A Remote Sensing and Artificial Neural Network-based Integrated Agricultural Drought Index: Index Development and Applications.” Catena 186: 104394. doi:10.1016/j.catena.2019.104394.
  • Lv, B., H. Yongtao, H. H. Chang, A. G. Russell, and Y. Bai. 2016. “Improving the Accuracy of Daily PM2. 5 Distributions Derived from the Fusion of Ground-level Measurements with Aerosol Optical Depth Observations, a Case Study in North China.” Environmental Science & Technology 50 (9): 4752–4759. doi:10.1021/acs.est.5b05940.
  • Mauri, E., P. Poulain, and Ž. Južnič‐Zonta. 2007. “MODIS Chlorophyll Variability in the Northern Adriatic Sea and Relationship with Forcing Parameters.” Journal of Geophysical Research: Oceans 112 (C3). doi:10.1029/2006JC003545.
  • Mohebzadeh, H., J. Yeom, and T. Lee. 2020. “Spatial Downscaling of MODIS Chlorophyll-a with Genetic Programming in South Korea.” Remote Sensing 12 (9): 1412. doi:10.3390/rs12091412.
  • Mohr, C. H., M. Manga, C.-Y. Wang, and O. Korup. 2017. “Regional Changes in Streamflow after a Megathrust Earthquake.” Earth and Planetary Science Letters 458: 418–428. doi:10.1016/j.epsl.2016.11.013.
  • Mountrakis, G., I. Jungho, and C. Ogole. 2011. “Support Vector Machines in Remote Sensing: A Review.” ISPRS Journal of Photogrammetry and Remote Sensing 66 (3): 247–259. doi:10.1016/j.isprsjprs.2010.11.001.
  • Naddafi, R., N. H. Koupayeh, and R. Ghorbani. 2021. “Spatial and Temporal Variations in Stable Isotope Values (δ13C and δ15N) of the Primary and Secondary Consumers along the Southern Coastline of the Caspian Sea.” Marine Pollution Bulletin 164: 112001. doi:10.1016/j.marpolbul.2021.112001.
  • Naghdi, K., M. Moradi, M. Rahimzadegan, K. Kabiri, and M. Rowshan Tabari. 2020. “Quantitative Modeling of Cyanobacterial Concentration Using MODIS Imagery in the Southern Caspian Sea.” Journal of Great Lakes Research 46 (5): 1251–1261. doi:10.1016/j.jglr.2020.07.003.
  • Nezlin, N. P. 2005. “Patterns of Seasonal and Interannual Variability of Remotely Sensed Chlorophyll.” In The Caspian Sea Environment, 143–157. Berlin, Heidelberg: Springer.
  • Nourani, V., A. Hosseini Baghanam, and M. Gebremichael. 2012. “Investigating the Ability of Artificial Neural Network (ANN) Models to Estimate Missing Rain-gauge Data.” Journal of Environmental Informatics 19 (1): 1. doi:10.3808/jei.201200207.
  • Pao-Shan, Y., T.-C. Yang, S.-Y. Chen, C.-M. Kuo, and H.-W. Tseng. 2017. “Comparison of Random Forests and Support Vector Machine for Real-time Radar-derived Rainfall Forecasting.” Journal of Hydrology 552: 92–104. doi:10.1016/j.jhydrol.2017.06.020.
  • Parisouj, P., H. Mohebzadeh, and T. Lee. 2020. “Employing Machine Learning Algorithms for Streamflow Prediction: A Case Study of Four River Basins with Different Climatic Zones in the United States.” Water Resources Management 34 (13): 4113–4131. doi:10.1007/s11269-020-02659-5.
  • Patel, S. S., and P. Ramachandran. 2015. “A Comparison of Machine Learning Techniques for Modeling River Flow Time Series: The Case of Upper Cauvery River Basin.” Water Resources Management 29 (2): 589–602. doi:10.1007/s11269-014-0705-0.
  • Peeters, F., R. Kipfer, D. Achermann, M. Hofer, W. Aeschbach-Hertig, U. Beyerle, D. M. Imboden, K. Rozanski, and F. Klaus. 2000. “Analysis of Deep-water Exchange in the Caspian Sea Based on Environmental Tracers.” Deep Sea Research Part I: Oceanographic Research Papers 47 (4): 621–654. doi:10.1016/S0967-0637(99)00066-7.
  • Poloczek, J., N. A. Treiber, and O. Kramer. 2014. “KNN Regression as Geo-imputation Method for Spatio-temporal Wind Data.” International Joint Conference SOCO’14-CISIS’14-ICEUTE’14, by de la Puerta J. et al. Advances in Intelligent Systems and Computing, vol 299. Springer, Cham. https://doi.org/10.1007/978-3-319-07995-0_19
  • Raje, D., and P. P. Mujumdar. 2011. “A Comparison of Three Methods for Downscaling Daily Precipitation in the Punjab Region.” Hydrological Processes 25 (23): 3575–3589. doi:10.1002/hyp.8083.
  • Ramo, R., M. García, D. Rodríguez, and E. Chuvieco. 2018. “A Data Mining Approach for Global Burned Area Mapping.” International Journal of Applied Earth Observation and Geoinformation 73: 39–51. doi:10.1016/j.jag.2018.05.027.
  • Robinson, I. S. 2004. Measuring the Oceans from Space: The Principles and Methods of Satellite Oceanography. Berlin, Heidelberg: Springer Science & Business Media.
  • Şahin, M. 2012. “Modelling of Air Temperature Using Remote Sensing and Artificial Neural Network in Turkey.” Advances in Space Research 50 (7): 973–985. doi:10.1016/j.asr.2012.06.021.
  • Schneider, T. 2001. “Analysis of Incomplete Climate Data: Estimation of Mean Values and Covariance Matrices and Imputation of Missing Values.” Journal of Climate 14 (5): 853–871. doi:10.1175/1520-0442(2001)014<0853:AOICDE>2.0.CO;2.
  • Şenkal, O. 2010. “Modeling of Solar Radiation Using Remote Sensing and Artificial Neural Network in Turkey.” Energy 35 (12): 4795–4801. doi:10.1016/j.energy.2010.09.009.
  • Shortridge, J. E., S. D. Guikema, and B. F. Zaitchik. 2016. “Machine Learning Methods for Empirical Streamflow Simulation: A Comparison of Model Accuracy, Interpretability, and Uncertainty in Seasonal Watersheds.” Hydrology & Earth System Sciences 20 (7): 7. doi:10.5194/hess-20-2611-2016.
  • Shrestha, N. K., and S. Shukla. 2015. “Support Vector Machine Based Modeling of Evapotranspiration Using Hydro-climatic Variables in a Sub-tropical Environment.” Agricultural and Forest Meteorology 200: 172–184. doi:10.1016/j.agrformet.2014.09.025.
  • Shukur, O. B., and M. H. Lee. 2015. “Imputation of Missing Values in Daily Wind Speed Data Using Hybrid AR-ANN Method.” Modern Applied Science 9 (11): 1. doi:10.5539/mas.v9n11p1.
  • Shunmugapandi, R., A. B. Inamdar, and S. K. Gedam. 2020. “Long-time-scale Investigation of Phytoplankton Communities Based on Their Size in the Arabian Sea.” International Journal of Remote Sensing 41 (15): 5992–6009. doi:10.1080/01431161.2020.1714785.
  • Smola, A. J., and S. Bernhard. 2004. “A Tutorial on Support Vector Regression.” Statistics and Computing 14 (3): 199–222. doi:10.1023/B:STCO.0000035301.49549.88.
  • Sorkhabi, O. M., J. Asgari, and A. Amiri-Simkooei. 2021. “Monitoring of Caspian Sea-level Changes Using Deep Learning-based 3D Reconstruction of GRACE Signal.” Measurement 174: 109004. doi:10.1016/j.measurement.2021.109004.
  • Strobl, C., A.-L. Boulesteix, T. Kneib, T. Augustin, and A. Zeileis. 2008. “Conditional Variable Importance for Random Forests.” BMC Bioinformatics 9 (1): 307. doi:10.1186/1471-2105-9-307.
  • Sudheer, K. P., A. K. Gosain, and K. S. Ramasastri. 2002. “A Data‐driven Algorithm for Constructing Artificial Neural Network Rainfall‐runoff Models.” Hydrological Processes 16 (6): 1325–1330. doi:10.1002/hyp.554.
  • Tongal, H., and M. J. Booij. 2018. “Simulation and Forecasting of Streamflows Using Machine Learning Models Coupled with Base Flow Separation.” Journal of Hydrology 564: 266–282. doi:10.1016/j.jhydrol.2018.07.004.
  • Toorani, M., A. A. Kakroodi, M. Yamani, and A. Naderi Beni. 2021. “Monitoring Shoreline Shift under Rapid Sea-level Change on the Caspian Sea Observed over 60 Years of Satellite and Aerial Photo Records.” Journal of Great Lakes Research 47 (3): 812–828. doi:10.1016/j.jglr.2021.02.006.
  • Vapnik, V. 2013. The Nature of Statistical Learning Theory. Berlin, Heidelberg: Springer science & business media.
  • Vapnik, V., and V. Vapnik. 1998. “Statistical Learning Theory Wiley.” New York 1: 624.
  • Vapnik, V. N. 1995. “The Nature of Statistical Learning.” Theory.
  • Waske, B., and J. A. Benediktsson. 2007. “Fusion of Support Vector Machines for Classification of Multisensor Data.” IEEE Transactions on Geoscience and Remote Sensing 45 (12): 3858–3866. doi:10.1109/TGRS.2007.898446.
  • Watanabe, F., E. Alcantara, T. Rodrigues, L. Rotta, N. Bernardo, and N. Imai. 2018. “Remote Sensing of the Chlorophyll-a Based on OLI/Landsat-8 and MSI/Sentinel-2A (Barra Bonita Reservoir, Brazil).” Anais Da Academia Brasileira De Ciências 90 (2 suppl 1): 1987–2000. doi:10.1590/0001-3765201720170125.
  • Xu, C., F. Dai, X. Xiwei, and Y. H. Lee. 2012. “GIS-based Support Vector Machine Modeling of Earthquake-triggered Landslide Susceptibility in the Jianjiang River Watershed, China.” Geomorphology 145: 70–80. doi:10.1016/j.geomorph.2011.12.040.
  • Yang, T., A. A. Asanjan, M. Faridzad, N. Hayatbini, X. Gao, and S. Sorooshian. 2017. “An Enhanced Artificial Neural Network with a Shuffled Complex Evolutionary Global Optimization with Principal Component Analysis.” Information Sciences 418: 302–316. doi:10.1016/j.ins.2017.08.003.
  • Yoon, H., Y. Hyun, and K.-K. Lee. 2007. “Forecasting Solute Breakthrough Curves through the Unsaturated Zone Using Artificial Neural Networks.” Journal of Hydrology 335 (1–2): 68–77. doi:10.1016/j.jhydrol.2006.11.001.
  • Zenkevitch, L. 1963. Biology of the Seas of the USSR. New York: Interscience Publishers.
  • Zhang, D., J. Lin, Q. Peng, D. Wang, T. Yang, S. Sorooshian, X. Liu, and J. Zhuang. 2018a. “Modeling and Simulating of Reservoir Operation Using the Artificial Neural Network, Support Vector Regression, Deep Learning Algorithm.” Journal of Hydrology 565: 720–736. doi:10.1016/j.jhydrol.2018.08.050.
  • Zhang, Z., X. Yang, L. Hao, L. Weide, H. Yan, and F. Shi. 2017. “Application of A Novel Hybrid Method for Spatiotemporal Data Imputation: A Case Study of the Minqin County Groundwater Level.” Journal of Hydrology 553: 384–397. doi:10.1016/j.jhydrol.2017.07.053.
  • Zonn, I. S. 2000. Three Centuries at the Caspian (The Synchronism of Major Historical Events of XVIII–20 Cc.), 1–72. Moscow.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.