References:
Adnan,
R.M. et al., 2023. Improved prediction of monthly streamflow in a
mountainous region by Metaheuristic-Enhanced deep learning and machine
learning models using hydroclimatic data. Theoretical and applied
climatology.
Almeida,
M., Pombo, S., Rebelo, R. and Coelho, P., 2021. The probability
distribution of daily streamflow in perennial rivers of Angola. Journal
of Hydrology, 603: 126869.
Araghinejad,
S., 2013. Data-Driven Modeling: Using MATLAB in Water Resources and
Environmental Engineering, 67. Springer Nature, Dordrecht.
ARNARI
S, W.S., 1999. Improving support vector machine classifiers by modfying
kernel functions. Neural Networks, 12(6): 783-789.
Atieh,
M., Taylor, G., M. A. Sattar, A. and Gharabaghi, B., 2017a. Prediction
of flow duration curves for ungauged basins. Journal of Hydrology, 545:
383-394.
Atieh,
M., Taylor, G., M. A. Sattar, A. and Gharabaghi, B., 2017b. Prediction
of flow duration curves for ungauged basins. Journal of Hydrology, 545:
383-394.
Atieh,
M., Taylor, G., M. A. Sattar, A. and Gharabaghi, B., 2017c. Prediction
of flow duration curves for ungauged basins. Journal of Hydrology, 545:
383-394.
Blöschl,
G., 2013. Runoff prediction in ungauged basins: synthesis across
processes, places and scales. Cambridge University Press.
Botter,
G., Peratoner, F., Porporato, A., Rodriguez Iturbe, I. and Rinaldo, A.,
2007. Signatures of large‐scale soil moisture dynamics on streamflow
statistics across U.S. climate regimes. Water Resources Research,
43(11).
Bozchaloei,
S.K. and Vafakhah, M., 2015. Regional Analysis of Flow Duration Curves
Using Adaptive Neuro-Fuzzy Inference System. Journal of Hydrologic
Engineering, 20(12): 06015008.
BREIMAN,
L., 2001. Random forests. Machine Learning, 45(1): 5-32.
Burgan,
H.I. and Aksoy, H., 2022a. Daily flow duration curve model for ungauged
intermittent subbasins of gauged rivers. Journal of Hydrology, 604:
127249.
Burgan,
H.I. and Aksoy, H., 2022b. Daily flow duration curve model for ungauged
intermittent subbasins of gauged rivers. Journal of Hydrology, 604:
127249.
Burgan,
H.I. and Aksoy, H., 2022c. Daily flow duration curve model for ungauged
intermittent subbasins of gauged rivers. Journal of Hydrology, 604:
127249.
Butcher,
J.B. et al., 2021. An Efficient Statistical Approach to Develop
Intensity-Duration-Frequency Curves for Precipitation and Runoff under
Future Climate. Clim Change, 164(1-2): 1-3.
Ceola,
S. et al., 2010. Comparative study of ecohydrological streamflow
probability distributions. Water Resources Research, 46(9).
Chang,
C.C. and Lin, C.J., 2001. Training nu-support vector classifiers: theory
and algorithms. Neural Comput, 13(9): 2119-47.
Chen,
T. and Guestrin, C., 2016. XGBoost: A Scalable Tree Boosting System.
ACM, Ithaca, pp. 785-794.
Cheng,
L. et al., 2012. Exploring the physical controls of regional patterns of
flow duration curves – Part 1: Insights from statistical analyses.
Hydrology and Earth System Sciences, 16(11): 4435-4446.
Choubin,
B., Darabi, H., Rahmati, O., Sajedi-Hosseini, F. and Kløve, B., 2018.
River suspended sediment modelling using the CART model: A comparative
study of machine learning techniques. Science of The Total Environment,
615: 272-281.
Cortez,
P. and Embrechts, M.J., 2013. Using sensitivity analysis and
visualization techniques to open black box data mining models.
Information Sciences, 225: 1-17.
Croker
K M, Y.A.R.Z., 2003. Flow duration curve estimation in ephemeral
catchments in Portugal. Hydrological sciences journal, 48(3): 427-439.
Crow,
W.T., Chen, F., Reichle, R.H., Xia, Y. and Liu, Q., 2018. Exploiting
Soil Moisture, Precipitation, and Streamflow Observations to Evaluate
Soil Moisture/Runoff Coupling in Land Surface Models. Geophysical
Research Letters, 45(10): 4869-4878.
Das,
S., Chakraborty, R. and Maitra, A., 2017. A random forest algorithm for
nowcasting of intense precipitation events. Advances in Space Research,
60(6): 1271-1282.
Dehghani,
M., Seifi, A. and Riahi-Madvar, H., 2019. Novel forecasting models for
immediate-short-term to long-term influent flow prediction by combining
ANFIS and grey wolf optimization. Journal of Hydrology, 576: 698-725.
Dikshit,
A. and Pradhan, B., 2021. Interpretable and explainable AI (XAI) model
for spatial drought prediction. Science of The Total Environment, 801:
149797.
Doulatyari,
B. et al., 2015. Predicting streamflow distributions and flow duration
curves from landscape and climate. Advances in Water Resources, 83:
285-298.
Engeland,
K. and Hisdal, H., 2009. A Comparison of Low Flow Estimates in Ungauged
Catchments Using Regional Regression and the HBV-Model. Water Resources
Management, 23(12): 2567-2586.
Esterhuizen,
J.A., Goldsmith, B.R., Linic, S. and Univ. Of Michigan, A.A.M.U., 2022.
Interpretable machine learning for knowledge generation in heterogeneous
catalysis. Nature catalysis, 5(3): 175-184.
Farmer,
W.H. and Vogel, R.M., 2016. On the deterministic and stochastic use of
hydrologic models. Water Resources Research, 52(7): 5619-5633.
Fatehi,
I., Amiri, B.J., Alizadeh, A. and Adamowski, J., 2015. Modeling the
Relationship between Catchment Attributes and In-stream Water Quality.
Water resources management, 29(14): 5055-5072.
Ghotbi,
S., Wang, D., Singh, A., Blöschl, G. and Sivapalan, M., 2020. A New
Framework for Exploring Process Controls of Flow Duration Curves. Water
Resources Research, 56(1).
Ghotbi,
S., Wang, D., Singh, A., Mayo, T. and Sivapalan, M., 2020. Climate and
Landscape Controls of Regional Patterns of Flow Duration Curves Across
the Continental United States: Statistical Approach. Water Resources
Research, 56(11).
Goodarzi,
M.R. and Vazirian, M., 2023. A geostatistical approach to estimate flow
duration curve parameters in ungauged basins. Applied Water Science,
13(9).
HUANG,
G., ZHU, Q. and SIEW, C., 2004. Extreme learning machine: a new learning
scheme of feedforward neural networks. IEEE, Piscataway NJ, pp. 985-990
vol.2.
Huang,
G., Zhu, Q. and Siew, C., 2006. Extreme learning machine: Theory and
applications. Neurocomputing, 70(1-3): 489-501.
Ibarra-Berastegi,
G., Saénz, J., Esnaola, G., Ezcurra, A. and Ulazia, A., 2015. Short-term
forecasting of the wave energy flux: Analogues, random forests, and
physics-based models. Ocean Engineering, 104: 530-539.
Karst,
N., Dralle, D. and Müller, M.F., 2019. On the Effect of Nonlinear
Recessions on Low Flow Variability: Diagnostic of an Analytical Model
for Annual Flow Duration Curves. Water Resources Research, 55(7):
6125-6137.
Khan,
M.Y.A., Hasan, F., Panwar, S. and Chakrapani, G.J., 2016. Neural network
model for discharge and water-level prediction for Ramganga River
catchment of Ganga Basin, India. Hydrological sciences journal, 61(11):
2084-2095.
Khan,
M.Y.A., Tian, F., Hasan, F. and Chakrapani, G.J., 2019. Artificial
neural network simulation for prediction of suspended sediment
concentration in the River Ramganga, Ganges Basin, India. International
Journal of Sediment Research, 34(2): 95-107.
Kim,
F.D.A.B., 2017. Towards A Rigorous Science of Interpretable Machine
Learning. arXiv, 2017.
Lee,
S.L.S., 2017. A Unified Approach to Interpreting Model Predictions.
NIPS.
Lehner,
B. et al., 2011. High-resolution mapping of the world’s reservoirs and
dams for sustainable river-flow management. Frontiers in ecology and the
environment, 9(9): 494-502.
Leong,
C. and Yokoo, Y., 2021. A step toward global-scale applicability and
transferability of flow duration curve studies: A flow duration curve
review (2000–2020). Journal of Hydrology, 603: 126984.
Ley,
A., Bormann, H. and Casper, M., 2023. Intercomparing LSTM and RNN to a
Conceptual Hydrological Model for a Low-Land River with a Focus on the
Flow Duration Curve. Water, 15(3): 505.
Li,
M., Shao, Q., Zhang, L. and Chiew, F.H.S., 2010. A new regionalization
approach and its application to predict flow duration curve in ungauged
basins. Journal of Hydrology, 389(1-2): 137-145.
Luan,
J., Liu, D., Lin, M. and Huang, Q., 2021. The construction of the flow
duration curve and the regionalization parameters analysis in the
northwest of China. Journal of Water and Climate Change, 12(6):
2639–2653.
Maier,
H.R. and Dandy, G.C., 2000. Neural networks for the prediction and
forecasting of water resources variables: a review of modelling issues
and applications. Environmental Modelling & Software, 15(1): 101-124.
Majnooni,
S. et al., 2023. Long-term precipitation prediction in different climate
divisions of California using remotely sensed data and machine learning.
Hydrological sciences journal: 1-25.
Mancini,
L.B.G.R., 2016. Regionalization of Flow-Duration Curves through
Catchment Classification with Streamflow Signatures and Physiographic –
Climate Indices. Journal of Hydrologic Engineering, 21(3): 05015027.
Manuel
Almeida, S.P.R.R., 2021. The probability distribution of daily
streamflow in perennial rivers of Angola. Journal of Hydrology, 603:
126869.
Maroufpoor,
S., Bozorg-Haddad, O. and Maroufpoor, E., 2020. Reference
evapotranspiration estimating based on optimal input combination and
hybrid artificial intelligent model: Hybridization of artificial neural
network with grey wolf optimizer algorithm. Journal of Hydrology, 588:
125060.
Mazvimavi,
D., Meijerink, A.M.J. and Stein, A., 2004. Prediction of base flows from
basin characteristics: a case study from Zimbabwe / Prévision de débits
de base à partir de caractéristiques du bassin: une étude de cas au
Zimbabwe. Hydrological sciences journal, 49(4): 715-715.
Mirjalili,
S., Mirjalili, S.M. and Lewis, A., 2014. Grey Wolf Optimizer. Advances
in Engineering Software, 69: 46-61.
Mohammadrezapour,
O., Piri, J. and Kisi, O., 2019. Comparison of SVM, ANFIS and GEP in
modeling monthly potential evapotranspiration in an arid region (Case
study: Sistan and Baluchestan Province, Iran). Water Supply, 19(2):
392-403.
Müller,
M.F. and Thompson, S.E., 2016. Comparing statistical and process-based
flow duration curve models in ungauged basins and changing rain regimes.
Hydrology and Earth System Sciences, 20(2): 669-683.
Müller,
M.F., Dralle, D.N. and Thompson, S.E., 2014. Analytical model for flow
duration curves in seasonally dry climates. Water Resources Research,
50(7): 5510-5531.
Nash,
J.E. and Sutcliffe, J.V., 1970. RIVER FLOW FORECASTING THROLIGH
CONCEPTUAL MODELS PART I - A DISCLISSION OF PRINCIPLES. ECOLOGICAL
MODELLING, 10(3): 0-290.
Nashwan,
M.S. and Shahid, S., 2019. Symmetrical uncertainty and random forest for
the evaluation of gridded precipitation and temperature data.
Atmospheric Research, 230: 104632.
Nearing,
G.S. and Gupta, H.V., 2015. The quantity and quality of information in
hydrologic models. Water Resources Research, 51(1): 524-538.
Over,
T.M., Farmer, W.H. and Russell, A.M., 2018. Refinement of a
regression-based method for prediction of flow-duration curves of daily
streamflow in the conterminous United States. Scientific Investigations
Report.
Poli,
R., Kennedy, J. and Blackwell, T., 2007. Particle swarm optimization.
Swarm Intelligence, 1(1): 33-57.
Poncelet,
C., Andréassian, V., Oudin, L. and Perrin, C., 2017. The Quantile
Solidarity approach for the parsimonious regionalization of flow
duration curves. Hydrological sciences journal, 62(9): 1364-1380.
Reichl,
F. and Hack, J., 2017. Derivation of Flow Duration Curves to Estimate
Hydropower Generation Potential in Data-Scarce Regions. Water, 9(8):
572.
Requena,
A.I., Ouarda, T.B.M.J. and Chebana, F., 2018. Low-flow frequency
analysis at ungauged sites based on regionally estimated streamflows.
Journal of hydrology (Amsterdam), 563: 523-532.
Rice,
J.S. and Emanuel, R.E., 2017. How are streamflow responses to the ElNino
Southern Oscillation affected by watershed characteristics? Water
Resources Research, 53(5): 4393-4406.
Schaefli,
B., Rinaldo, A. and Botter, G., 2013. Analytic probability distributions
for snow‐dominated streamflow. Water Resources Research, 49(5):
2701-2713.
Searcy,
J.K., 1959. Flow‐duration curves, Manual of hydrology. U.S. Geological
Survey.
Seifi,
A. and Soroush, F., 2020. Pan evaporation estimation and derivation of
explicit optimized equations by novel hybrid meta-heuristic ANN based
methods in different climates of Iran. Computers and Electronics in
Agriculture, 173: 105418.
Sharifi
Garmdareh, E., Vafakhah, M. and Eslamian, S.S., 2018. Regional flood
frequency analysis using support vector regression in arid and semi-arid
regions of Iran. Hydrological sciences journal, 63(3): 426-440.
Shen,
C., 2018. A Transdisciplinary Review of Deep Learning Research and Its
Relevance for Water Resources Scientists. Water Resources Research,
54(11): 8558-8593.
Shin,
Y. and Park, J., 2023. Modeling climate extremes using the
four-parameter kappa distribution for r-largest order statistics.
Weather and Climate Extremes, 39: 100533.
Vafakhah,
M. and Khosrobeigi Bozchaloei, S., 2020. Regional Analysis of Flow
Duration Curves through Support Vector Regression. Water resources
management, 34(1): 283-294.
Vaheddoost,
B., Yilmaz, M.U. and Safari, M.J.S., 2023. Estimation of flow duration
and mass flow curves in ungauged tributary streams. Journal of Cleaner
Production, 409: 137246.
VAPNIK
V, G.S.E.S., 1997. Support Vector Method for Function Approximation,
Regression Estimation, and Signal Processing, Advances in Neura
l lnformation Processing Systems, pp. 281-287.
Veber
Costa, W.F.Â.S., 2020. Identifying Regional Models for Flow Duration
Curves with Evolutionary Polynomial Regression: Application for
Intermittent Streams. Journal of Hydrologic Engineering, 25(1):
04019059.
Worland,
S.C., Steinschneider, S., Asquith, W., Knight, R. and Wieczorek, M.,
2019. Prediction and Inference of Flow Duration Curves Using Multioutput
Neural Networks. Water Resources Research, 55(8): 6850-6868.
Ye,
S., Yaeger, M., Coopersmith, E., Cheng, L. and Sivapalan, M., 2012.
Exploring the physical controls of regional patterns of flow duration
curves – Part 2: Role of seasonality, the regime curve, and associated
process controls. Hydrology and Earth System Sciences, 16(11):
4447-4465.
Yokoo,
Y. and Sivapalan, M., 2011. Towards reconstruction of the flow duration
curve: development of a conceptual framework with a physical basis.
Hydrology and Earth System Sciences, 15(9): 2805-2819.
Yu
Zhou, Y.Z., 2023. Physical controls of regional distribution patterns of
precipitation and flow duration curves in the middle and lower reaches
of the Yangtze River. Authorea.
Zhou,
J. et al., 2021. Optimization of support vector machine through the use
of metaheuristic algorithms in forecasting TBM advance rate. Engineering
Applications of Artificial Intelligence, 97: 104015.