Developing reservoir monthly inflow forecasts using artificial intelligence and climate phenomenon information

Yang, Tiantian; Asanjan, Ata Akbari; Welles, Edwin; Gao, Xiaogang; Sorooshian, Soroosh; Liu, Xiaomang

doi:10.1002/2017WR020482

Title: Developing reservoir monthly inflow forecasts using artificial intelligence and climate phenomenon information

Journal Article · Thu Mar 09 00:00:00 EST 2017 · Water Resources Research

DOI:https://doi.org/10.1002/2017WR020482· OSTI ID:1466038

^[1];

^[2]; Welles, Edwin ^[3]; Gao, Xiaogang ^[2];

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Univ. of California, Irvine, CA (United States); Deltares USA Inc., Silver Springs, MD (United States)
Univ. of California, Irvine, CA (United States)
Deltares USA Inc., Silver Springs, MD (United States)
Chinese Academy of Sciences (CAS), Beijing (China)

Abstract Reservoirs are fundamental human‐built infrastructures that collect, store, and deliver fresh surface water in a timely manner for many purposes. Efficient reservoir operation requires policy makers and operators to understand how reservoir inflows are changing under different hydrological and climatic conditions to enable forecast‐informed operations. Over the last decade, the uses of Artificial Intelligence and Data Mining [AI & DM] techniques in assisting reservoir streamflow subseasonal to seasonal forecasts have been increasing. In this study, Random Forest [RF), Artificial Neural Network (ANN), and Support Vector Regression (SVR) are employed and compared with respect to their capabilities for predicting 1 month‐ahead reservoir inflows for two headwater reservoirs in USA and China. Both current and lagged hydrological information and 17 known climate phenomenon indices, i.e., PDO and ENSO, etc., are selected as predictors for simulating reservoir inflows. Results show (1) three methods are capable of providing monthly reservoir inflows with satisfactory statistics; (2) the results obtained by Random Forest have the best statistical performances compared with the other two methods; (3) another advantage of Random Forest algorithm is its capability of interpreting raw model inputs; (4) climate phenomenon indices are useful in assisting monthly or seasonal forecasts of reservoir inflow; and (5) different climate conditions are autocorrelated with up to several months, and the climatic information and their lags are cross correlated with local hydrological conditions in our case studies.

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Research Organization:: Univ. of California, Irvine, CA (United States)

Sponsoring Organization:: USDOE

Grant/Contract Number:: IA0000018; DE‐IA0000018

OSTI ID:: 1466038

Alternate ID(s):: OSTI ID: 1402404

Journal Information:: Water Resources Research, Vol. 53, Issue 4; ISSN 0043-1397

Publisher:: American Geophysical Union (AGU)Copyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 196 works

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References (85)

Model Evaluation Guidelines for Systematic Quantification of Accuracy in Watershed Simulations Transactions of the ASABE, Vol. 50, Issue 3 https://doi.org/10.13031/2013.23153	journal	January 2007
Decision trees and decision-making Quinlan, J. R. IEEE Transactions on Systems, Man, and Cybernetics, Vol. 20, Issue 2 https://doi.org/10.1109/21.52545	journal	January 1990
Historical and National Perspectives on Extreme West Coast Precipitation Associated with Atmospheric Rivers during December 2010 Ralph, F. M.; Dettinger, M. D. Bulletin of the American Meteorological Society, Vol. 93, Issue 6 https://doi.org/10.1175/BAMS-D-11-00188.1	journal	June 2012
The Tropical Western Hemisphere Warm Pool Wang, Chunzai; Enfield, David B. Geophysical Research Letters, Vol. 28, Issue 8 https://doi.org/10.1029/2000GL011763	journal	April 2001
A further comparison of splitting rules for decision-tree induction Buntine, Wray; Niblett, Tim Machine Learning, Vol. 8, Issue 1 https://doi.org/10.1007/BF00994006	journal	January 1992
Feature deduction and ensemble design of intrusion detection systems Chebrolu, Srilatha; Abraham, Ajith; Thomas, Johnson P. Computers & Security, Vol. 24, Issue 4 https://doi.org/10.1016/j.cose.2004.09.008	journal	June 2005
Streamflow Forecasting Using Different Artificial Neural Network Algorithms Kişi, Özgür Journal of Hydrologic Engineering, Vol. 12, Issue 5 https://doi.org/10.1061/(ASCE)1084-0699(2007)12:5(532)	journal	September 2007
A tutorial on support vector regression Smola, Alex J.; Schölkopf, Bernhard Statistics and Computing, Vol. 14, Issue 3 https://doi.org/10.1023/B:STCO.0000035301.49549.88	journal	August 2004
Species Distribution Models: Ecological Explanation and Prediction Across Space and Time Elith, Jane; Leathwick, John R. Annual Review of Ecology, Evolution, and Systematics, Vol. 40, Issue 1 https://doi.org/10.1146/annurev.ecolsys.110308.120159	journal	December 2009
Simulating California reservoir operation using the classification and regression-tree algorithm combined with a shuffled cross-validation scheme: SIMULATING RESERVOIR OPERATION IN CALIFORNIA Yang, Tiantian; Gao, Xiaogang; Sorooshian, Soroosh Water Resources Research, Vol. 52, Issue 3 https://doi.org/10.1002/2015WR017394	journal	March 2016
Induction of decision trees Quinlan, J. R. Machine Learning, Vol. 1, Issue 1, p. 81-106 https://doi.org/10.1007/BF00116251	journal	March 1986
Surface Climate and Streamflow Variability in the Western United States and Their Relationship to Large-Scale Circulation Indices Redmond, Kelly T.; Koch, Roy W. Water Resources Research, Vol. 27, Issue 9 https://doi.org/10.1029/91WR00690	journal	September 1991
Image processing with neural networks—a review Egmont-Petersen, M.; de Ridder, D.; Handels, H. Pattern Recognition, Vol. 35, Issue 10 https://doi.org/10.1016/S0031-3203(01)00178-9	journal	October 2002
An overview of statistical learning theory Vapnik, V. N. IEEE Transactions on Neural Networks, Vol. 10, Issue 5 https://doi.org/10.1109/72.788640	journal	January 1999
Advancing monthly streamflow prediction accuracy of CART models using ensemble learning paradigms Erdal, Halil Ibrahim; Karakurt, Onur Journal of Hydrology, Vol. 477 https://doi.org/10.1016/j.jhydrol.2012.11.015	journal	January 2013
Filling the gaps: Calibrating a rainfall-runoff model using satellite-derived surface water extent Revilla-Romero, Beatriz; Beck, Hylke E.; Burek, Peter Remote Sensing of Environment, Vol. 171 https://doi.org/10.1016/j.rse.2015.10.022	journal	December 2015
Forecasting daily streamflow using online sequential extreme learning machines Lima, Aranildo R.; Cannon, Alex J.; Hsieh, William W. Journal of Hydrology, Vol. 537 https://doi.org/10.1016/j.jhydrol.2016.03.017	journal	June 2016
The effect of ENSO on the intraseasonal variance of surface temperatures in winter Smith, Catherine A.; Sardeshmukh, Prashant D. International Journal of Climatology, Vol. 20, Issue 13 https://doi.org/10.1002/1097-0088(20001115)20:13<1543::AID-JOC579>3.0.CO;2-A	journal	January 2000
Atmospheric rivers induced heavy precipitation and flooding in the western U.S. simulated by the WRF regional climate model: ATMOSPHERIC RIVER, PRECIPITATION, FLOOD Leung, L. Ruby; Qian, Yun Geophysical Research Letters, Vol. 36, Issue 3 https://doi.org/10.1029/2008GL036445	journal	February 2009
Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling Gupta, Hoshin V.; Kling, Harald; Yilmaz, Koray K. Journal of Hydrology, Vol. 377, Issue 1-2 https://doi.org/10.1016/j.jhydrol.2009.08.003	journal	October 2009
Evolutionary artificial neural networks: a review Ding, Shifei; Li, Hui; Su, Chunyang Artificial Intelligence Review, Vol. 39, Issue 3 https://doi.org/10.1007/s10462-011-9270-6	journal	June 2011
Forecasting daily streamflow using hybrid ANN models Wang, Wen; Gelder, Pieter H. A. J. M. Van; Vrijling, J. K. Journal of Hydrology, Vol. 324, Issue 1-4 https://doi.org/10.1016/j.jhydrol.2005.09.032	journal	June 2006
A performance comparison of modern statistical techniques for molecular descriptor selection and retention prediction in chromatographic QSRR studies Hancock, Tim; Put, Raf; Coomans, Danny Chemometrics and Intelligent Laboratory Systems, Vol. 76, Issue 2 https://doi.org/10.1016/j.chemolab.2004.11.001	journal	April 2005
El Niño-Southern Oscillation and water resources in the headwaters region of the Yellow River: links and potential for forecasting Lü, A.; Jia, S.; Zhu, W. Hydrology and Earth System Sciences, Vol. 15, Issue 4 https://doi.org/10.5194/hess-15-1273-2011	journal	January 2011
One-Day-Ahead Streamflow Forecasting Using Artificial Neural Networks and a Meteorological Mesoscale Model Linares-Rodriguez, Alvaro; Lara-Fanego, Vicente; Pozo-Vazquez, David Journal of Hydrologic Engineering, Vol. 20, Issue 9 https://doi.org/10.1061/(ASCE)HE.1943-5584.0001163	journal	September 2015
Revised Surface‐Water Supply Index for Western United States Garen, David C. Journal of Water Resources Planning and Management, Vol. 119, Issue 4 https://doi.org/10.1061/(ASCE)0733-9496(1993)119:4(437)	journal	July 1993
Building more realistic reservoir optimization models using data mining – A case study of Shelbyville Reservoir Hejazi, Mohamad I.; Cai, Ximing Advances in Water Resources, Vol. 34, Issue 6 https://doi.org/10.1016/j.advwatres.2011.03.001	journal	June 2011
A comparison of Artificial Neural Networks (ANN) and Local Linear Regression (LLR) techniques for predicting monthly reservoir levels Shamim, Muhammad Ali; Hassan, Muhammad; Ahmad, Sameel KSCE Journal of Civil Engineering, Vol. 20, Issue 2 https://doi.org/10.1007/s12205-015-0298-z	journal	June 2015
Climate Change, Atmospheric Rivers, and Floods in California - A Multimodel Analysis of Storm Frequency and Magnitude Changes1: Climate Change, Atmospheric Rivers, and Floods in California - A Multimodel Analysis of Storm Frequency and Magnitude Changes Dettinger, Michael JAWRA Journal of the American Water Resources Association, Vol. 47, Issue 3 https://doi.org/10.1111/j.1752-1688.2011.00546.x	journal	June 2011
Discretized and Continuous Target Fields for the Reservoir Release Rules During Floods Wei, Chih-Chiang Water Resources Management, Vol. 26, Issue 12 https://doi.org/10.1007/s11269-012-0085-2	journal	June 2012
Ensemble forecasting of species distributions Araujo, M.; New, M. Trends in Ecology & Evolution, Vol. 22, Issue 1 https://doi.org/10.1016/j.tree.2006.09.010	journal	January 2007
Heuristic Methods for Reservoir Monthly Inflow Forecasting: A Case Study of Xinfengjiang Reservoir in Pearl River, China Cheng, Chun-Tian; Feng, Zhong-Kai; Niu, Wen-Jing Water, Vol. 7, Issue 12 https://doi.org/10.3390/w7084477	journal	August 2015
Implicit stochastic optimization with data mining for reservoir system operation Li, Xue-Zhen; Xu, Li-Zhong; Chen, Yan-Guo 2010 International Conference on Machine Learning and Cybernetics (ICMLC 2010) https://doi.org/10.1109/ICMLC.2010.5580718	conference	July 2010
Review article. Studying climate effects on ecology through the use of climate indices: the North Atlantic Oscillation, El Niño Southern Oscillation and beyond Stenseth, Nils Chr.; Ottersen, Geir; Hurrell, James W. Proceedings of the Royal Society of London. Series B: Biological Sciences, Vol. 270, Issue 1529 https://doi.org/10.1098/rspb.2003.2415	journal	October 2003
Statistical pattern recognition: a review Jain, A. K.; Duin, P. W. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 22, Issue 1 https://doi.org/10.1109/34.824819	journal	January 2000
Predictability of horizontal water vapor transport relative to precipitation: Enhancing situational awareness for forecasting western U.S. extreme precipitation and flooding: PREDICTABILITY OF WESTERN U.S. EXTREMES Lavers, David A.; Waliser, Duane E.; Ralph, F. Martin Geophysical Research Letters, Vol. 43, Issue 5 https://doi.org/10.1002/2016GL067765	journal	March 2016
Application of ANN, Fuzzy Logic and Decision Tree Algorithms for the Development of Reservoir Operating Rules kumar, A. R. Senthil; Goyal, Manish Kumar; Ojha, C. S. P. Water Resources Management, Vol. 27, Issue 3 https://doi.org/10.1007/s11269-012-0225-8	journal	December 2012
Prediction of regional streamflow frequency using model tree ensembles Schnier, Spencer; Cai, Ximing Journal of Hydrology, Vol. 517 https://doi.org/10.1016/j.jhydrol.2014.05.029	journal	September 2014
Evolving artificial neural networks Proceedings of the IEEE, Vol. 87, Issue 9 https://doi.org/10.1109/5.784219	journal	January 1999
Application of ANN for Reservoir Inflow Prediction and Operation Jain, S. K.; Das, A.; Srivastava, D. K. Journal of Water Resources Planning and Management, Vol. 125, Issue 5 https://doi.org/10.1061/(ASCE)0733-9496(1999)125:5(263)	journal	September 1999
Using large-scale climatic patterns for improving long lead time streamflow forecasts for Gunnison and San Juan River Basins: USING LARGE-SCALE CLIMATIC PATTERNS FOR IMPROVING STREAMFLOW FORECASTS Kalra, Ajay; Miller, William P.; Lamb, Kenneth W. Hydrological Processes, Vol. 27, Issue 11 https://doi.org/10.1002/hyp.9236	journal	May 2012
Potential of support vector regression for prediction of monthly streamflow using endogenous property Maity, Rajib; Bhagwat, Parag P.; Bhatnagar, Ashish Hydrological Processes, Vol. 24, Issue 7 https://doi.org/10.1002/hyp.7535	journal	March 2010
The Northern Oscillation Index (NOI): a new climate index for the northeast Pacific Schwing, F. B.; Murphree, T.; Green, P. M. Progress in Oceanography, Vol. 53, Issue 2-4 https://doi.org/10.1016/S0079-6611(02)00027-7	journal	January 2002
Economic Value of Long-Lead Streamflow Forecasts for Columbia River Hydropower Hamlet, Alan F.; Huppert, Daniel; Lettenmaier, Dennis P. Journal of Water Resources Planning and Management, Vol. 128, Issue 2 https://doi.org/10.1061/(ASCE)0733-9496(2002)128:2(91)	journal	March 2002
Assessing the predictive capability of randomized tree-based ensembles in streamflow modelling Galelli, S.; Castelletti, A. Hydrology and Earth System Sciences, Vol. 17, Issue 7 https://doi.org/10.5194/hess-17-2669-2013	journal	January 2013
Monthly streamflow forecasting based on improved support vector machine model Guo, Jun; Zhou, Jianzhong; Qin, Hui Expert Systems with Applications, Vol. 38, Issue 10 https://doi.org/10.1016/j.eswa.2011.04.114	journal	September 2011
Regime-shifting streamflow processes: Implications for water supply reservoir operations: REGIME-SHIFTING STREAMFLOW AND WATER RESERVOIR OPERATIONS Turner, S. W. D.; Galelli, S. Water Resources Research, Vol. 52, Issue 5 https://doi.org/10.1002/2015WR017913	journal	May 2016
Optimal tree-based release rules for real-time flood control operations on a multipurpose multireservoir system Wei, Chih-Chiang; Hsu, Nien-Sheng Journal of Hydrology, Vol. 365, Issue 3-4 https://doi.org/10.1016/j.jhydrol.2008.11.038	journal	February 2009
River Stage Forecasting Using Artificial Neural Networks Thirumalaiah, Konda; Deo, M. C. Journal of Hydrologic Engineering, Vol. 3, Issue 1 https://doi.org/10.1061/(ASCE)1084-0699(1998)3:1(26)	journal	January 1998
Bagging predictors Breiman, Leo Machine Learning, Vol. 24, Issue 2, p. 123-140 https://doi.org/10.1007/BF00058655	journal	August 1996
Application of artificial neural network, fuzzy logic and decision tree algorithms for modelling of streamflow at Kasol in India Senthil kumar, A. R.; Goyal, Manish Kumar; Ojha, C. S. P. Water Science and Technology, Vol. 68, Issue 12 https://doi.org/10.2166/wst.2013.491	journal	October 2013
Biases of April 1 snow water equivalent records in the Sierra Nevada and their associations with large-scale climate indices Montoya, E. L.; Dozier, J.; Meiring, W. Geophysical Research Letters, Vol. 41, Issue 16 https://doi.org/10.1002/2014GL060588	journal	August 2014
Artificial intelligence based models for stream-flow forecasting: 2000–2015 Yaseen, Zaher Mundher; El-shafie, Ahmed; Jaafar, Othman Journal of Hydrology, Vol. 530 https://doi.org/10.1016/j.jhydrol.2015.10.038	journal	November 2015
Training of artificial neural networks using differential evolution algorithm Slowik, Adam; Bialko, Michal 2008 Conference on Human System Interactions (HSI) https://doi.org/10.1109/HSI.2008.4581409	conference	May 2008
Atmospheric Rivers, Floods and the Water Resources of California Dettinger, Michael D.; Ralph, Fred Martin; Das, Tapash Water, Vol. 3, Issue 2 https://doi.org/10.3390/w3020445	journal	March 2011
Re-Operation of Major Reservoirs for Flood and Environmental Management Burley, Nate; Maher, Katherine; Lund, Jay World Environmental And Water Resources Congress 2012 https://doi.org/10.1061/9780784412312.241	conference	July 2012
Water Reservoir Control with Data Mining Bessler, Florian T.; Savic, Dragan A.; Walters, Godfrey A. Journal of Water Resources Planning and Management, Vol. 129, Issue 1 https://doi.org/10.1061/(ASCE)0733-9496(2003)129:1(26)	journal	January 2003
Measuring the strength of ENSO events: How does 1997/98 rank? Wolter, Klaus; Timlin, Michael S. Weather, Vol. 53, Issue 9 https://doi.org/10.1002/j.1477-8696.1998.tb06408.x	journal	September 1998
Variable selection by Random Forests using data with missing values Hapfelmeier, A.; Ulm, K. Computational Statistics & Data Analysis, Vol. 80 https://doi.org/10.1016/j.csda.2014.06.017	journal	December 2014
An analysis of the feasibility of long-range streamflow forecasting for Colombia using El Niño–Southern Oscillation indicators Gutiérrez, F.; Dracup, J. A. Journal of Hydrology, Vol. 246, Issue 1-4 https://doi.org/10.1016/S0022-1694(01)00373-0	journal	June 2001
A Pacific Interdecadal Climate Oscillation with Impacts on Salmon Production Mantua, Nathan J.; Hare, Steven R.; Zhang, Yuan Bulletin of the American Meteorological Society, Vol. 78, Issue 6 https://doi.org/10.1175/1520-0477(1997)078<1069:APICOW>2.0.CO;2	journal	June 1997
Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks Hsu, Kou-lin; Gao, Xiaogang; Sorooshian, Soroosh Journal of Applied Meteorology, Vol. 36, Issue 9 https://doi.org/10.1175/1520-0450(1997)036<1176:PEFRSI>2.0.CO;2	journal	September 1997
Artificial neural networks: a tutorial Jain, A. K.; Mohiuddin, K. M. Computer, Vol. 29, Issue 3 https://doi.org/10.1109/2.485891	journal	March 1996
An empirical comparison of selection measures for decision-tree induction Mingers, John Machine Learning, Vol. 3, Issue 4 https://doi.org/10.1007/BF00116837	journal	March 1989
Multi-time scale stream flow predictions: The support vector machines approach Asefa, Tirusew; Kemblowski, Mariush; McKee, Mac Journal of Hydrology, Vol. 318, Issue 1-4 https://doi.org/10.1016/j.jhydrol.2005.06.001	journal	March 2006
A new node splitting measure for decision tree construction Chandra, B.; Kothari, Ravi; Paul, Pallath Pattern Recognition, Vol. 43, Issue 8 https://doi.org/10.1016/j.patcog.2010.02.025	journal	August 2010
River flow forecasting through conceptual models part I — A discussion of principles Nash, J. E.; Sutcliffe, J. V. Journal of Hydrology, Vol. 10, Issue 3 https://doi.org/10.1016/0022-1694(70)90255-6	journal	April 1970
Short term streamflow forecasting using artificial neural networks Zealand, Cameron M.; Burn, Donald H.; Simonovic, Slobodan P. Journal of Hydrology, Vol. 214, Issue 1-4 https://doi.org/10.1016/S0022-1694(98)00242-X	journal	January 1999
Decision-tree analysis on optimal release of reservoir storage under typhoon warnings Cheng, Chang-Chi; Hsu, Nien-Sheng; Wei, Chih-Chiang Natural Hazards, Vol. 44, Issue 1 https://doi.org/10.1007/s11069-007-9142-1	journal	July 2007
Artificial neural networks as a tool in ecological modelling, an introduction Lek, Sovan; Guégan, J. F. Ecological Modelling, Vol. 120, Issue 2-3 https://doi.org/10.1016/S0304-3800(99)00092-7	journal	August 1999
Using support vector machines for long-term discharge prediction Lin, Jian-Yi; Cheng, Chun-Tian; Chau, Kwok-Wing Hydrological Sciences Journal, Vol. 51, Issue 4 https://doi.org/10.1623/hysj.51.4.599	journal	August 2006
Artificial neural networks Hopfield, J. J. IEEE Circuits and Devices Magazine, Vol. 4, Issue 5 https://doi.org/10.1109/101.8118	journal	September 1988
Input variable selection for water resources systems using a modified minimum redundancy maximum relevance (mMRMR) algorithm Hejazi, Mohamad I.; Cai, Ximing Advances in Water Resources, Vol. 32, Issue 4 https://doi.org/10.1016/j.advwatres.2009.01.009	journal	April 2009
Multivariate Reservoir Inflow Forecasting Using Temporal Neural Networks Coulibaly, Paulin; Anctil, François; Bobée, Bernard Journal of Hydrologic Engineering, Vol. 6, Issue 5 https://doi.org/10.1061/(ASCE)1084-0699(2001)6:5(367)	journal	October 2001
Predicting monthly streamflow using data-driven models coupled with data-preprocessing techniques: PREDICTING MONTHLY STREAMFLOW USING DATA-DRIVEN MODELS Wu, C. L.; Chau, K. W.; Li, Y. S. Water Resources Research, Vol. 45, Issue 8 https://doi.org/10.1029/2007WR006737	journal	August 2009
Columbia River Streamflow Forecasting Based on ENSO and PDO Climate Signals Hamlet, Alan F.; Lettenmaier, Dennis P. Journal of Water Resources Planning and Management, Vol. 125, Issue 6 https://doi.org/10.1061/(ASCE)0733-9496(1999)125:6(333)	journal	November 1999
Random Forests Breiman, Leo Machine Learning, Vol. 45, Issue 1, p. 5-32 https://doi.org/10.1023/A:1010933404324	journal	January 2001
Artificial Neural Networks Fortuna, Luigi; Rizzotto, Gianguido; Lavorgna, Mario Advanced Textbooks in Control and Signal Processing https://doi.org/10.1007/978-1-4471-0357-8_4	book	January 2001
miRNALoc: predicting miRNA subcellular localizations based on principal component scores of physico-chemical properties and pseudo compositions of di-nucleotides Meher, Prabina Kumar; Satpathy, Subhrajit; Rao, Atmakuri Ramakrishna Scientific Reports, Vol. 10, Issue 1 https://doi.org/10.1038/s41598-020-71381-4	journal	September 2020
Evolving Artificial Neural Networks Downing, Keith L. Intelligence Emerging https://doi.org/10.7551/mitpress/9898.003.0014	book	January 2015
Artificial Neural Networks Buscema, Paolo Massimo; Massini, Giulia; Breda, Marco Artificial Adaptive Systems Using Auto Contractive Maps https://doi.org/10.1007/978-3-319-75049-1_2	book	January 2018
Evolving artificial neural networks Porto, V. William; Fogel, David B. Clinical Applications of Artificial Neural Networks https://doi.org/10.1017/cbo9780511543494.010	book	August 2001
Assessing the predictive capability of randomized tree-based ensembles in streamflow modelling Galelli, S.; Castelletti, A. Hydrology and Earth System Sciences Discussions https://doi.org/10.5194/hessd-10-1617-2013	posted_content	January 2013
Artificial Neural Networks Makhoul, John INVESTIGATIVE RADIOLOGY, Vol. 25, Issue 6 https://doi.org/10.1097/00004424-199006000-00027	journal	January 1990
Artificial Neural Networks Gad, Ahmed Fawzy Practical Computer Vision Applications Using Deep Learning with CNNs https://doi.org/10.1007/978-1-4842-4167-7_2	book	January 2018

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Combination of Multiple Data-Driven Models for Long-Term Monthly Runoff Predictions Based on Bayesian Model Averaging Huang, Huaping; Liang, Zhongmin; Li, Binquan Water Resources Management, Vol. 33, Issue 9 https://doi.org/10.1007/s11269-019-02305-9	journal	June 2019
An effective deep learning neural network model for short‐term load forecasting Li, Ning; Wang, Lu; Li, Xinquan Concurrency and Computation: Practice and Experience, Vol. 32, Issue 7 https://doi.org/10.1002/cpe.5595	journal	November 2019
Assessing the Applicability of Random Forest, Stochastic Gradient Boosted Model, and Extreme Learning Machine Methods to the Quantitative Precipitation Estimation of the Radar Data: A Case Study to Gwangdeoksan Radar, South Korea, in 2018 Shin, Ju-Young; Ro, Yonghun; Cha, Joo-Wan Advances in Meteorology, Vol. 2019 https://doi.org/10.1155/2019/6542410	journal	October 2019
Real-time reservoir operation using data mining techniques Bozorg-Haddad, Omid; Aboutalebi, Mahyar; Ashofteh, Parisa-Sadat Environmental Monitoring and Assessment, Vol. 190, Issue 10 https://doi.org/10.1007/s10661-018-6970-2	journal	September 2018
Tracking the fate of deposited nitrogen and its redistribution in a subtropical watershed in China Gao, Yang; Hao, Zhuo; Han, Ning Ecohydrology, Vol. 12, Issue 5 https://doi.org/10.1002/eco.2094	journal	April 2019
Resilience, Decision‐making, and Environmental Water Releases Chu, Long; Grafton, R. Quentin; Stewardson, Michael Earth's Future, Vol. 6, Issue 6 https://doi.org/10.1029/2018ef000879	journal	June 2018
A Brief Review of Random Forests for Water Scientists and Practitioners and Their Recent History in Water Resources Tyralis, Hristos; Papacharalampous, Georgia; Langousis, Andreas Water, Vol. 11, Issue 5 https://doi.org/10.3390/w11050910	journal	April 2019
The Use of Large-Scale Climate Indices in Monthly Reservoir Inflow Forecasting and Its Application on Time Series and Artificial Intelligence Models Kim, Taereem; Shin, Ju-Young; Kim, Hanbeen Water, Vol. 11, Issue 2 https://doi.org/10.3390/w11020374	journal	February 2019

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