Enhanced streamflow simulations using nudging based optimization coupled with data-driven and hydrological models
Tài liệu tham khảo
Abda, 2022, Assessing machine learning models for streamflow estimation: a case study in Oued Sebaou watershed (Northern Algeria), Hydrol. Sci. J., 10.1080/02626667.2022.2083511
Abdulkabir, 2015, An empirical study of generalized linear model for count data, J. Appl. Comput. Math., 04, 3
Adnan, 2021, Novel ensemble forecasting of streamflow using locally weighted learning algorithm, Sustain, 13
Ahmed, 2020, Multi-model ensemble predictions of precipitation and temperature using machine learning algorithms, Atmos. Res., 236, 10.1016/j.atmosres.2019.104806
Alizadeh, 2020, Simulating monthly streamflow using a hybrid feature selection approach integrated with an intelligence model, Hydrol. Sci. J., 65, 1374, 10.1080/02626667.2020.1755436
Arnold, 1998, Large area hydrologic modeling and assessment part I: model development, J. Am. Assoc. Am. WATER Resour. Assoc., 34, 73, 10.1111/j.1752-1688.1998.tb05961.x
Behera, 2020, Predicting land use and land cover scenario in Indian national river basin: the Ganga, Trop. Ecol., 61, 51, 10.1007/s42965-020-00073-x
Berbić, 2022, Optimization of supervised learning models for modeling of mean monthly flows, Neural Comput. Appl., 10.1007/s00521-022-07406-y
Bisht, 2020, Impact of climate change on streamflow regime of a large Indian river basin using a novel monthly hybrid bias correction technique and a conceptual modeling framework, J. Hydrol., 590, 10.1016/j.jhydrol.2020.125448
Boucher, 2020, Data assimilation for streamflow forecasting using extreme learning machines and multilayer perceptrons, Water Resour. Res., 56, 1, 10.1029/2019WR026226
Breiman, 2001, Random forests, Mach. Learn, 45, 5, 10.1023/A:1010933404324
Ciabatta, 2015, Integration of satellite soil moisture and rainfall observations over the italian territory, J. Hydrometeorol., 16, 1341, 10.1175/JHM-D-14-0108.1
Di Virgilio, 2020, Realised added value in dynamical downscaling of Australian climate change, Clim. Dyn., 54, 4675, 10.1007/s00382-020-05250-1
Essenfelder, 2020, A coupled hydrologic-machine learning modelling framework to support hydrologic modelling in river basins under Interbasin Water Transfer regimes, Environmental Modelling & Software, 131, 10.1016/j.envsoft.2020.104779
Flato, G., MarotzkeMarotzke, J., Abiodun, B., Braconnot, P., Chou, S.C., Collins, W., Cox, P., Driouech, F., Emori, S., V. Eyring, C., Forest, P.G., Guilyardi, E., Jakob, C., Kattsov, V., Reason, C., Rummukainen, M., 2013. IPCC Technical Summary. Clim. Chang. 2013 Phys. Sci. Basis. Contrib. Work. Gr. I to Fifth Assess. Rep. Intergov. Panel Clim. Chang.
Gharbia, 2022, Hybrid data-driven models for hydrological simulation and projection on the catchment scale, Sustain, 14
Guevara-Ochoa, 2020, Spatio-temporal effect of climate change on water balance and interactions between groundwater and surface water in plains, Sci. Total Environ., 722
Hastie, 2001
Jayasree, 2012, Evaluating the changes in water resources due to the impact of man-made modifications for the Varahi river basin, Karnataka, 5, 863
Kannan, 2013, A nonparametric kernel regression model for downscaling multisite daily precipitation in the Mahanadi basin, Water Resour. Res., 49, 1360, 10.1002/wrcr.20118
Kilinc, 2022, A hybrid model for streamflow forecasting in the basin of euphrates, Water (Switz. ), 14
Kim, 2016, Climate change and uncertainty assessment over a hydroclimatic transect of Michigan, Stoch. Environ. Res. Risk Assess., 30, 923, 10.1007/s00477-015-1097-2
Kolluru, 2021, A machine learning approach for deriving spectral absorption coefficients of optically active oceanic constituents, Comput. Geosci., 155, 10.1016/j.cageo.2021.104879
Kolluru, 2021, A neural network approach for deriving absorption coefficients of ocean water constituents from total light absorption and particulate absorption coefficients, Comput. Geosci., 147, 10.1016/j.cageo.2020.104678
Kolluru, 2021, Development and evaluation of pre and post integration techniques for enhancing drought predictions over India, Int. J. Clim., 41, 4804, 10.1002/joc.7100
Kolluru, 2020, Evaluation and integration of reanalysis rainfall products under contrasting climatic conditions in India, Atmos. Res., 246, 10.1016/j.atmosres.2020.105121
Kolluru, 2020, Secondary precipitation estimate merging using machine learning: development and evaluation over Krishna river basin, India, Remote Sens., 12, 10.3390/rs12183013
Konapala, 2020, Machine learning assisted hybrid models can improve streamflow simulation in diverse catchments across the conterminous US, Environ. Res. Lett., 15, 10.1088/1748-9326/aba927
Kuhn, M., Weston, S., Keefer, C., Coulter, N., Quinlan, R., 2017. Package ‘Cubist.’
Li, 2019, Tree-ring-width based streamflow reconstruction based on the random forest algorithm for the source region of the Yangtze River, China, Catena, 183, 10.1016/j.catena.2019.104216
Liu, 2022, Ensemble streamflow forecasting over a cascade reservoir catchment with integrated hydrometeorological modeling and machine learning, Hydrol. Earth Syst. Sci., 26, 265, 10.5194/hess-26-265-2022
Loizu, 2018, Advances in water resources on the assimilation set-up of ASCAT soil moisture data for improving stream fl ow catchment simulation, Adv. Water Resour., 111, 86, 10.1016/j.advwatres.2017.10.034
Mani, 2020, Evaluation of satellite precipitation products in simulating streamflow in a humid tropical catchment of india using a semi-distributed hydrological model, Water, 12, 1
Massari, 2014, Advances in water resources potential of soil moisture observations in flood modelling: estimating initial conditions and correcting rainfall, Adv. Water Resour., 74, 44, 10.1016/j.advwatres.2014.08.004
Mazzoleni, 2018, Real-time assimilation of streamflow observations into a hydrological routing model: effects of model structures and updating methods, Hydrol. Sci. J., 63, 386, 10.1080/02626667.2018.1430898
Moriasi, 2015, Hydrologic and water quality models: performance measures and evaluation criteria, Am. Soc. Agric. Biol. Eng., 58, 1763
Mudbhatkal, 2018, Regional climate trends and topographic influence over the Western Ghat catchments of India, Int. J. Climatol., 38, 2265, 10.1002/joc.5333
Mudbhatkal, 2017, Impacts of climate change on varied river-flow regimes of southern india, J. Hydrol. Eng., 22, 10.1061/(ASCE)HE.1943-5584.0001556
Mukherjee, 2018, Climate change and drought: a perspective on drought indices, Curr. Clim. Chang. Rep., 4, 145, 10.1007/s40641-018-0098-x
Nearing, 2021, What role does hydrological science play in the age of machine learning, Water Resour. Res., 57, 10.1029/2020WR028091
Ni, 2020, Streamflow forecasting using extreme gradient boosting model coupled with Gaussian mixture model, J. Hydrol., 586, 10.1016/j.jhydrol.2020.124901
Quinlan, 1993
Read, 2019, Process-guided deep learning predictions of lake water temperature, Water Resour. Res., 55, 9173, 10.1029/2019WR024922
Reshmidevi, 2014, Modelling the impact of extensive irrigation on the groundwater resources, Hydrol. Process., 28, 628, 10.1002/hyp.9615
Salvi, 2013, High-resolution multisite daily rainfall projections in India with statistical downscaling for climate change impacts assessment, J. Geophys. Res. Atmos., 118, 3557, 10.1002/jgrd.50280
Sariev, 2020, Bayesian regularized artificial neural networks for the estimation of the probability of default, Quant. Financ., 20, 311, 10.1080/14697688.2019.1633014
Sazib, 2020, Exploring spatiotemporal relations between soil moisture, precipitation, and streamflow for a large set of watersheds using google earth engine, Water (Switz.), 12, 1
Schapire, R.E., 2003. The Boosting Approach to Machine Learning: An Overview 149–171. 〈https://doi.org/10.1007/978–0-387–21579-2_9〉.
Schyns, 2019, Limits to the world’s green water resources for food, feed, fiber, timber, and bioenergy, Proc. Natl. Acad. Sci. U. S. A., 116, 4893, 10.1073/pnas.1817380116
Sharannya, T.M., Sreelakshmi, C.M., Drissia, T.K., 2016. Discharge Simulation for Thuthapuzha Subbasin of Bharathapuzha River Basin in Kerala, in: International Conference on Hydraulics, Water Resources and Coastal Engineering (Hydro2016), CWPRS Pune, India. pp. 699–704.
Sharannya, 2021, Effects of land use and climate change on water scarcity in rivers of the Western Ghats of India, Environ. Monit. Assess., 193, 10.1007/s10661-021-09598-7
Sharannya, 2018, Assessing climate change impacts on river hydrology – a case study in the Western Ghats of India, J. Earth Syst. Sci., 127, 1, 10.1007/s12040-018-0979-3
Shetty, 2015, Effect of water quality on the composition of fish communities in three coastal rivers of Karnataka, India, Int. J. Aquat. Biol., 3, 42
Sikorska-Senoner, 2021, A novel ensemble-based conceptual-data-driven approach for improved streamflow simulations, Environ. Model. Softw., 143, 10.1016/j.envsoft.2021.105094
Sinha, 2020, Assessing the impacts of historical and future land use and climate change on the streamflow and sediment yield of a tropical mountainous river basin in South India, Environ. Monit. Assess., 192, 10.1007/s10661-020-08623-5
Sinha, 2020, Assessing the impacts of land use/land cover and climate change on surface runoff of a humid tropical river basin in Western Ghats, India, Int. J. River Basin Manag., 0, 1, 10.1080/15715124.2020.1809434
Swain, 2020, Water scarcity-risk assessment in data-scarce river basins under decadal climate change using a hydrological modelling approach, J. Hydrol., 590, 10.1016/j.jhydrol.2020.125260
Ursulak, 2021, Integration of hydrological models with entropy and multi-objective optimization based methods for designing specific needs streamflow monitoring networks, J. Hydrol., 593, 10.1016/j.jhydrol.2020.125876
Usman, 2021, Impacts of climate change on the streamflow of a large river basin in the Australian tropics using optimally selected climate model outputs, J. Clean. Prod., 315, 10.1016/j.jclepro.2021.128091
Uttarwar, 2020, Bivariate modeling of hydroclimatic variables in humid tropical coastal region using archimedean copulas, J. Hydrol. Eng., 25, 05020026, 10.1061/(ASCE)HE.1943-5584.0001981
Venkatesh, 2020, Evaluating the effects of forest fire on water balance using fire susceptibility maps, Ecol. Indic., 110, 10.1016/j.ecolind.2019.105856
Venkatesh, 2020, Modelling stream flow and soil erosion response considering varied land practices in a cascading river basin, J. Environ. Manag., 264, 10.1016/j.jenvman.2020.110448
Venkatesh, 2022, Optimal ranges of social-environmental drivers and their impacts on vegetation dynamics in Kazakhstan, Sci. Total Environ., 847, 10.1016/j.scitotenv.2022.157562
Wagle, 2020, Multi-temporal land cover change mapping using google earth engine and ensemble learning methods, Appl. Sci., 10, 1, 10.3390/app10228083
Xu, 2022, Scale effects of the monthly streamflow prediction using a state ‑ of ‑ the ‑ art deep learning model, Water Resour. Manag., 10.1007/s11269-022-03216-y
Yang, 2020, A physical process and machine learning combined hydrological model for daily streamflow simulations of large watersheds with limited observation data, J. Hydrol., 590, 10.1016/j.jhydrol.2020.125206
Zhang, 2020, Evaluation and integration of the top-down and bottom-up satellite precipitation products over mainland China, J. Hydrol., 581, 10.1016/j.jhydrol.2019.124456
