Modelling seasonal flow regime and environmental flow in Punarbhaba river of India and Bangladesh
Tài liệu tham khảo
Adamowski, 2010, Development of a coupled wavelet transform and neural network method for flow forecasting of non-perennial rivers in semiarid watersheds, J. Hydrol., 390, 85, 10.1016/j.jhydrol.2010.06.033
Adel, 2001, Effect on water resources from upstream water diversion in the Ganges basin, J. Environ. Qual., 30, 356, 10.2134/jeq2001.302356x
Adnan, 2017, Stream flow forecasting using artificial neural network and support vector machine models, American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS), 29, 286
Afzal, 2015, The impact of projected changes in climate variability on the reliability of surface water supply in Scotland, Water Sci. Technol. Water Supply, 15, 736, 10.2166/ws.2015.027
Amezquita-Sanchez, 2015, A new music-empirical wavelet transform methodology for time–frequency analysis of noisy nonlinear and non-stationary signals, Digit. Signal Process., 45, 55, 10.1016/j.dsp.2015.06.013
Andalib, 2019, Application of wavelet denoising and artificial intelligence models for stream flow forecasting, Adv. Res. Civ. Eng., 1, 1
Angarita, 2018, Basin-scale impacts of hydropower development on the Mompós Depression wetlands, Colombia, Hydrol. Earth Syst. Sci., 22, 2839, 10.5194/hess-22-2839-2018
Arantes, 2019, Impacts of hydroelectric dams on fishes and fisheries in tropical rivers through the lens of functional traits, Curr. Opin. Environ. Sustain., 37, 28, 10.1016/j.cosust.2019.04.009
Armstrong, 1992, Error measures for generalizing about forecasting methods: empirical comparisons, Int. J. Forecast., 8, 69, 10.1016/0169-2070(92)90008-W
Asefa, 2006, Multi-time scale stream flow predictions: the support vector machines approach, J. Hydrol., 318, 7, 10.1016/j.jhydrol.2005.06.001
Ayazpour, 2018, Combined sewer flow prediction using hybrid wavelet artificial neural network model, 693
Barzegar, 2016, Application of wavelet-artificial intelligence hybrid models for water quality prediction: a case study in Aji-Chay River, Iran, Stoch. Environ. Res. Risk Assess., 30, 1797, 10.1007/s00477-016-1213-y
Beck, 2012, Environmental and livelihood impacts of dams: common lessons across development gradients that challenge sustainability, Int. J. River Basin Manag., 10, 73, 10.1080/15715124.2012.656133
Bhandari, 2019, Streamflow forecasting using singular value decomposition and support vector machine for the upper Rio Grande river basin, JAWRA J. American Water Resour. Assoc., 55, 680, 10.1111/1752-1688.12733
Breiman, 2001, Random forests, Mach. Learn., 45, 5, 10.1023/A:1010933404324
Bunn, 2002, Basic principles and ecological consequences of altered flow regimes for aquatic biodiversity, J. Environ. Manag., 30, 492
Chen, 2015, A comparative study of population-based optimization algorithms for downstream river flow forecasting by a hybrid neural network model, Eng. Appl. Artif. Intell., 46, 258, 10.1016/j.engappai.2015.09.010
Chong, 2019, Wavelet transform based method for river stream flow time series frequency analysis and assessment in tropical environment, Water Resour. Manag., 33, 2015, 10.1007/s11269-019-02226-7
Crook, 2015, Human effects on ecological connectivity in aquatic ecosystems: integrating scientific approaches to support management and mitigation, Sci. Total Environ., 534, 52, 10.1016/j.scitotenv.2015.04.034
Dang, 2016, Hydrological alterations from water infrastructure development in the Mekong floodplains, Hydrol. Process., 30, 3824, 10.1002/hyp.10894
Dang, 2018, Future hydrological alterations in the Mekong Delta under the impact of water resources development, land subsidence and sea level rise, J. Hydrol.: Reg. Stud., 15, 119
DasGupta, 2013, Cumulative impacts of human interventions and climate change on Mangrove ecosystems of South and Southeast Asia: an overview, J. Ecosyst., 2013, 15
Deo, 2017, Drought forecasting in eastern Australia using multivariate adaptive regression spline, least square support vector machine and M5Tree model, Atmos. Res., 184, 149, 10.1016/j.atmosres.2016.10.004
Ebrahimi, 2017, Simulation of groundwater level variations using wavelet combined with neural network, linear regression and support vector machine, Glob. Planet. Chang., 148, 181, 10.1016/j.gloplacha.2016.11.014
Forio, 2015, Bayesian belief network models to analyse and predict ecological water quality in rivers, Ecol. Model., 312, 222, 10.1016/j.ecolmodel.2015.05.025
Freire, 2019, Analysis of the use of discrete wavelet transforms coupled with ANN for short-term streamflow forecasting, Appl. Soft Comput., 80, 494, 10.1016/j.asoc.2019.04.024
Gain, 2014, Impact of the Farakka dam on thresholds of the hydrologic flow regime in the lower Ganges river basin (Bangladesh), Water, 6, 2501, 10.3390/w6082501
Gao, 2012, Hydrologic, ecologic and livelihood impact assessment of a system of small reservoirs in Ghana
Gao, 2013, Impact of climate change and anthropogenic activities on streamflow and sediment discharge in the Wei River basin, China, Hydrol. Earth Syst. Sci., 17, 961, 10.5194/hess-17-961-2013
Garro, 2016, Classification of DNA microarrays using artificial neural networks and ABC algorithm, Appl. Soft Comput., 38, 548, 10.1016/j.asoc.2015.10.002
Garsole, 2015, Streamflow forecasting by using support vector regression
Gong, 2016, A comparative study of artificial neural networks, support vector machines and adaptive neuro fuzzy inference system for forecasting groundwater levels near Lake Okeechobee, Florida, Water Resour. Manag., 30, 375, 10.1007/s11269-015-1167-8
Gorgij, 2016, Groundwater budget forecasting, using hybrid wavelet-ANN-GP modelling: a case study of Azarshahr Plain, East Azerbaijan, Iran, Nord. Hydrol, 48, 455, 10.2166/nh.2016.202
Goupillaud, 1984, Cycle-octave and related transforms in seismic signal analysis, Geoexploration, 23, 85, 10.1016/0016-7142(84)90025-5
Hadi, 2018, Streamflow forecasting using four wavelet transformation combinations approaches with data-driven models: a comparative study, Water Resour. Manag., 32, 4661, 10.1007/s11269-018-2077-3
Haguma, 2017, Seasonal streamflow forecast with machine learning and teleconnection indices in the context non-stationary climate
Hecht, 2018, Hydropower dams of the Mekong River basin: a review of their hydrological impacts, J. Hydrol., 568, 285, 10.1016/j.jhydrol.2018.10.045
Hossain, 2005, Fish species composition in the river Padma near Rajshahi, J. Life Earth Sci., 1, 35
IPCC, 2014
Jiang, 2014, Assessment of hydrologic alterations caused by the Three Gorges Dam in the middle and lower reaches of Yangtze River, China, Water, 6, 1419, 10.3390/w6051419
Kalteh, 2016, Improving forecasting accuracy of streamflow time series using least squares support vector machine coupled with data-preprocessing techniques, Water Resour. Manag., 30, 747, 10.1007/s11269-015-1188-3
Karim, 2015, Assessing the impacts of climate change and dams on floodplain inundation and wetland connectivity in the wet–dry tropics of northern Australia, J. Hydrol., 522, 80, 10.1016/j.jhydrol.2014.12.005
Kisi, 2015, Streamflow forecasting and estimation using least square support vector regression and adaptive neuro-fuzzy embedded fuzzy c-means clustering, Water Resour. Manag., 29, 5109, 10.1007/s11269-015-1107-7
Kisi, 2007, Comparison of different ANN techniques in river flow prediction, Civ. Eng. Environ. Syst., 24, 211, 10.1080/10286600600888565
Kumar, 2015, Reservoir inflow forecasting using ensemble models based on neural networks, wavelet analysis and bootstrap method, Water Resour. Manag., 29, 4863, 10.1007/s11269-015-1095-7
Lamouroux, 2015, The ecological restoration of large rivers needs science-based, predictive tools meeting public expectations: an overview of the R hône project, Freshw. Biol., 60, 1069, 10.1111/fwb.12553
Li, 2009, Impacts of land use change and climate variability on hydrology in an agricultural catchment on the Loess Plateau of China, J. Hydrol., 337, 35, 10.1016/j.jhydrol.2009.08.007
Li, 2014, Temporal change analysis based on data characteristics and nonparametric test, Water Resour., 28, 227
Li, 2015, Multifractal detrended fluctuation analysis of streamflow in the Yellow River Basin, China, Water, 7, 1670, 10.3390/w7041670
Lin, 2017, Inter-and intra-annual environmental flow alteration and its implication in the Pearl River Delta, South China, J. Hydro-Environ. Res., 15, 27, 10.1016/j.jher.2017.01.002
Liu, 2016, Seismic time-frequency analysis via empirical wavelet transform, IEEE Geosci. Remote Sens. Lett., 13, 28, 10.1109/LGRS.2015.2493198
Liu, 2017, Long-term streamflow forecasting based on relevance vector machine model, Water, 9, 9, 10.3390/w9010009
Lu, 2018, Quantifying the impacts of small dam construction on hydrological alterations in the Jiulong River basin of Southeast China, J. Hydrol., 567, 382, 10.1016/j.jhydrol.2018.10.034
Machado, 2011, Monthly rainfall–runoff modelling using artificial neural networks, Hydrol. Sci. J. – J. Sci. Hydrol., 56, 349, 10.1080/02626667.2011.559949
Mailhot, 2018, Assessing the potential impacts of dam operation on daily flow at ungauged river reaches, J. Hydrol.: Reg. Stud., 18, 156
Mazumder, 2017, Behaviour and training of river near bridges and barrages: some case studies, 263
Mittal, 2016, Impact of human intervention and climate change on natural flow regime, Water Resour. Manag., 30, 685, 10.1007/s11269-015-1185-6
Modaresi, 2018, A comparative assessment of artificial neural network, generalized regression neural network, least-square support vector regression, and K-nearest neighbor regression for monthly streamflow forecasting in linear and nonlinear conditions, Water Resour. Manag., 32, 243, 10.1007/s11269-017-1807-2
Murshed, 2019, Changes in hydrology of the Ganges delta of Bangladesh and corresponding impacts on water resources, JAWRA. J. Am. Water Resour. Assoc., 55, 800, 10.1111/1752-1688.12775
Nievergelt, 2001
Pal, 2018, Identifying dam-induced wetland changes using an inundation frequency approach: the case of the Atreyee River basin of Indo-Bangladesh, Ecohydrol. Hydrobiol., 18, 66, 10.1016/j.ecohyd.2017.11.001
Pal, 2018, Application of frequency ratio and logistic regression models for assessing physical wetland vulnerability in Punarbhaba river basin of Indo Bangladesh, Hum. Ecol. Risk Assess. Int. J., 1
Pal, 2019, Impact of missing flow on active inundation areas and transformation of parafluvial wetlands in Punarbhaba–Tangon river basin of Indo-Bangladesh, Geocarto Int., 34, 1055, 10.1080/10106049.2018.1469676
Papacharalampous, 2017, Forecasting of geophysical processes using stochastic and machine learning algorithms, European Water, 59, 161
Papadaki, 2016, Potential impacts of climate change on flow regime and fish habitat in mountain rivers of the south-western Balkans, Sci. Total Environ., 540, 418, 10.1016/j.scitotenv.2015.06.134
Partal, 2016, Wavelet regression and wavelet neural network models for forecasting monthly streamflow, Journal of Water and Climate Change, 8, 48, 10.2166/wcc.2016.091
Pearse-Smith, 2012
Peng, 2017, Streamflow forecasting using empirical wavelet transform and artificial neural networks, Water, 9, 406, 10.3390/w9060406
Percival, 1999
Pham, 2019, Combing random forest and least square support vector regression for improving extreme rainfall downscaling, Water, 11, 451, 10.3390/w11030451
Piqué, 2016, Hydrological characterization of dammed rivers in the NW Mediterranean region, Hydrol. Process., 30, 1691, 10.1002/hyp.10728
Poff, 2018, Beyond the natural flow regime? Broadening the hydro-ecological foundation to meet environmental flows challenges in a non-stationary world, Freshw. Biol., 63, 1011, 10.1111/fwb.13038
Polikar, 2012, Ensemble learning
Rahman, 2017, 1
Rashid, 2015, Evidences of neotectonic activities as reflected by drainage characteristics of the Mahananda river floodplain and its adjoining areas, Bangladesh, Am. J. Earth Sci., 2, 61
Remesan, 2016
Remo, 2018, Assessing the impacts of dams and levees on the hydrologic record of the Middle and Lower Mississippi River, USA, Geomorphology, 313, 88, 10.1016/j.geomorph.2018.01.004
Rezaeianzadeh, 2014, Flood flow forecasting using ANN, ANFIS and regression models, Neural Comput. Appl., 25, 25, 10.1007/s00521-013-1443-6
Richter, 1996, A method for assessing hydrologic alteration within ecosystems, Conserv. Biol., 10, 1163, 10.1046/j.1523-1739.1996.10041163.x
Richter, 1997, How much water does a river need?, Freshw. Biol., 37, 231, 10.1046/j.1365-2427.1997.00153.x
Saha, 2018, Emerging conflict between agriculture extension and physical existence of wetland in post-dam period in Atreyee River basin of Indo-Bangladesh, Environ. Dev. Sustain.
Saha, 2019, Exploring physical wetland vulnerability of Atreyee river basin in India and Bangladesh using logistic regression and fuzzy logic approaches, Ecol. Indicat., 98, 251, 10.1016/j.ecolind.2018.11.009
Sang, 2008, New method for estimating periods in hydrologic series data, 645
Santos, 2018, Wavelet-based variability on streamflow at 40-year timescale in the Black Sea region of Turkey, Arabian Journal of Geosciences, 11, 169, 10.1007/s12517-018-3514-6
Schneider, 2017, Hydrological threats to riparian wetlands of international importance–a global quantitative and qualitative analysis, Hydrol. Earth Syst. Sci., 21, 2799, 10.5194/hess-21-2799-2017
Seo, 2015, Daily water level forecasting using wavelet decomposition and artificial intelligence techniques, J. Hydrol., 520, 224, 10.1016/j.jhydrol.2014.11.050
Shabri, 2015, A hybrid model for stream flow forecasting using wavelet and least Squares support vector machines, J. Teknologi, 73
Shafaei, 2016, Lake level forecasting using wavelet-SVR, wavelet-ANFIS and wavelet-ARMA conjunction models, Water Resour. Manag., 30, 79, 10.1007/s11269-015-1147-z
Shenify, 2016, Precipitation estimation using support vector machine with discrete wavelet transform, Water Resour. Manag., 30, 641, 10.1007/s11269-015-1182-9
Shiri, 2018, Improving the performance of the mass transfer-based reference evapotranspiration estimation approaches through a coupled wavelet-random forest methodology, J. Hydrol., 561, 737, 10.1016/j.jhydrol.2018.04.042
Smakhtin, 2006
Stone, 2017, Evaluating the impacts of hydrologic and geomorphic alterations on floodplain connectivity, Ecohydrology, 10, 10.1002/eco.1833
Su, 2018, Long-term trends in global river flow and the causal relationships between river flow and ocean signals, J. Hydrol., 563, 818, 10.1016/j.jhydrol.2018.06.058
Swain, 1996, Displacing the conflict: environmental destruction in Bangladesh and ethnic conflict in India, J. Peace Res., 33, 189, 10.1177/0022343396033002005
Syvitski, 2013, Anthropocene metamorphosis of the Indus Delta and lower floodplain, Anthropocene, 3, 24, 10.1016/j.ancene.2014.02.003
Talukdar, 2017, Impact of dam on flow regime and flood plain modification in Punarbhaba river basin of Indo-Bangladesh barind tract, Water Conserv. Sci. Eng
Talukdar, 2017, Impact of dam on inundation regime of flood plain wetland of punarbhaba river basin of barind tract of Indo-Bangladesh, Int. Soil and Water Conserv. Res., 10.1016/j.iswcr.2017.05.003
Talukdar, 2019, Effects of damming on the hydrological regime of Punarbhaba river basin wetlands, Ecol. Eng., 135, 61, 10.1016/j.ecoleng.2019.05.014
Thakur, 2017, Using wavelet to analyze periodicities in hydrologic variables, vol. 2017, 499
Tiwari, 2018, Flood forecasting and uncertainty assessment using wavelet-and bootstrap-based neural networks, 74
Tongal, 2018, Simulation and forecasting of streamflows using machine learning models coupled with base flow separation, J. Hydrol., 564, 266, 10.1016/j.jhydrol.2018.07.004
Visessri, 2016, Uncertainty in flow time-series predictions in a tropical monsoon-dominated catchment in northern Thailand, J. Hydrol. Eng., 21, 10.1061/(ASCE)HE.1943-5584.0001407
Walling, 2003, Recent trends in the suspended sediment loads of the world’s rivers, Glob. Planet. Chang., 39, 111, 10.1016/S0921-8181(03)00020-1
Wang, 2018, Application of stationary wavelet entropy in pathological brain detection, Multimed. Tools Appl., 77, 3701, 10.1007/s11042-016-3401-7
Wu, 2017, Detecting the quantitative hydrological response to changes in climate and human activities, Sci. Total Environ., 586, 328, 10.1016/j.scitotenv.2017.02.010
Xie, 2015, The impact of Three Gorges Dam on the downstream eco-hydrological environment and vegetation distribution of East Dongting Lake, Ecohydrology, 8, 738, 10.1002/eco.1543
Xue, 2017, Quantitative assessment of hydrological alteration caused by irrigation projects in the Tarim River basin, China, Sci. Rep., 7, 4291, 10.1038/s41598-017-04583-y
Yang, 2016, Lake hydrology, water quality and ecology impacts of altered river–lake interactions: advances in research on the middle Yangtze river, Nord. Hydrol, 47, 1, 10.2166/nh.2016.003
Yaseen, 2015, Artificial intelligence based models for stream-flow forecasting: 2000–2015, J. Hydrol., 530, 829, 10.1016/j.jhydrol.2015.10.038
Yaseen, 2016, Stream-flow forecasting using extreme learning machines: a case study in a semi-arid region in Iraq, J. Hydrol., 542, 603, 10.1016/j.jhydrol.2016.09.035
Yu, 2008, Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm, Energy Econ., 30, 2623, 10.1016/j.eneco.2008.05.003
Yuan, 2015, Hydro climatic trend and periodicity for the source region of the Yellow River, J. Hydrol. Eng., 20, 10.1061/(ASCE)HE.1943-5584.0001182
Zhang, 2015, Analysis of streamflow variations in the Heihe River Basin, northwest China: trends, abrupt changes, driving factors and ecological influences, J HydrolReg Stud, 3, 106
Zhang, 2016, Impact of the three Gorges dam on the hydrology and ecology of the Yangtze river, Water, 8, 590, 10.3390/w8120590
Zhang, 2018, Univariate streamflow forecasting using commonly used data-driven models: literature review and case study, Hydrol. Sci. J., 63, 1091, 10.1080/02626667.2018.1469756
Zhu, 2016, Streamflow estimation by support vector machine coupled with different methods of time series decomposition in the upper reaches of Yangtze River, China, Environ. Earth Sci., 75, 531, 10.1007/s12665-016-5337-7