References
- Al-Abadi, A.M. and Shahid, S., 2016. Spatial mapping of artesian zone at Iraqi southern desert using a GIS-based random forest machine learning model. Modeling Earth Systems and Environment, 2 (2), 96.
- Alizamir, M., Kisi, O., and Zounemat-Kermani, M., 2018. Modelling long-term groundwater fluctuations by extreme learning machine using hydro-climatic data. Hydrological Sciences Journal, 63 (1), 63–73.
- Babovic, V., 2005. Data mining in hydrology. Hydrological Processes, 19 (7), 1511–1515.
- Babovic, V. and Keijzer, M., 2000. Forecasting of river discharges in the presence of chaos and noise. Flood Issues in Contemporary Water Management, 71, 405–419.
- Bai, Z., et al., 2014. Sparse extreme learning machine for classification. IEEE Transactions on Cybernetics, 44 (10), 1858–1870. doi:10.1109/TCYB.2014.2298235
- Breiman, L., 2001. Random forests. Machine Learning, 45 (1), 5–32.
- Cutler, D.R., et al., 2007. Random forests for classification in ecology. Ecology, 88 (11), 2783–2792.
- Deo, R.C., et al., 2017. Forecasting effective drought index using a wavelet extreme learning machine (W-ELM) model. Stochastic Environmental Research and Risk Assessment, 31 (5), 1211–1240.
- Deo, R.C. and Şahin, M., 2015. Application of the extreme learning machine algorithm for the prediction of monthly effective drought index in eastern Australia. Atmospheric Research, 153, 512–525.
- Di, C., Yang, X., and Wang, X., 2014. A four-stage hybrid model for hydrological time series forecasting. PloS One, 9 (8), e104663.
- Diamantidis, N., Karlis, D., and Giakoumakis, E.A., 2000. Unsupervised stratification of cross-validation for accuracy estimation. Artificial Intelligence, 116 (1), 1–16.
- Gao, P., et al., 2013. Impact of climate change and anthropogenic activities on stream flow and sediment discharge in the Wei River basin, China. Hydrology and Earth System Sciences, 17 (3), 961–972.
- Gershenfeld, N.A. and Weigend, A.S., 1994. The future of time series: learning and understanding. In: A.S. Weigend and N.A. Gershenfeld, eds. Time series prediction: forecasting the future and understanding the past. Reading, MA: Addison-Wesley, 1–70.
- Goyal, M.K., Ojha, C.S., and Burn, D.H., 2017. Sustainable water resources management. In: Machine learning algorithms and their application in water resources management. Reston, VA: American Society of Civil Engineers, 165–178.
- Grange, S.K., et al., 2018. Random forest meteorological normalisation models for Swiss PM 10 trend analysis. Atmospheric Chemistry and Physics, 18 (9), 6223–6239.
- Huang, G.-B., et al., 2012. Extreme learning machine for regression and multiclass classification. IEEE Transactions on Systems, Man, and Cybernetics, Part B (cybernetics), 42 (2), 513–529.
- Huang, G.-B., 2014a. An insight into extreme learning machines: random neurons, random features and kernels. Cognitive Computation, 6 (3), 376–390.
- Huang, G.-B., Ding, X., and Zhou, H., 2010. Optimization method based extreme learning machine for classification. Neurocomputing, 74 (1), 155–163.
- Huang, G.-B., Zhu, Q.-Y., and Siew, C.-K., 2004. Extreme learning machine: a new learning scheme of feedforward neural networks[J]. Neural Networks, 2 (3), 985–990.
- Huang, G.-B., Zhu, Q.-Y., and Siew, C.-K., 2006. Extreme learning machine: theory and applications. Neurocomputing, 70 (1), 489–501.
- Huang, N., Lu, G., and Xu, D., 2016. A permutation importance-based feature selection method for short-term electricity load forecasting using random forest. Energies, 9 (10), 767.
- Huang, P.-S., et al. 2013. Random features for kernel deep convex network. In: 2013 IEEE international conference on acoustics, speech and signal processing. Vancouver, BC, Canada: IEEE, 3143–3147.
- Huang, S., et al., 2014b. Monthly streamflow prediction using modified EMD-based support vector machine. Journal of Hydrology, 511, 764–775.
- Jaiantilal, A., 2009. Classification and regression by randomforest-matlab. Available from: http://code.Google.Com/p/randomforest-matlab
- Jian, Y., et al., 2017. A novel extreme learning machine classification model for e-Nose application based on the multiple kernel approach. Sensors, 17 (6), 1434.
- Karran, D.J., Morin, E., and Adamowski, J., 2014. Multi-step streamflow forecasting using data-driven non-linear methods in contrasting climate regimes. Journal of Hydroinformatics, 16 (3), 671–689.
- Kennel, M.B., Brown, R., and Abarbanel, H.D., 1992. Determining embedding dimension for phase-space reconstruction using a geometrical construction. Physical Review A, 45 (6), 3403.
- Kisi, O. and Alizamir, M., 2018. Modelling reference evapotranspiration using a new wavelet conjunction heuristic method: wavelet extreme learning machine vs wavelet neural networks. Agricultural and Forest Meteorology, 263, 41–48.
- Le, Q., Sarlós, T., and Smola, A., 2013. Fastfood-approximating kernel expansions in loglinear time. In Proceedings of the international conference on machine learning. Bellevue, Washington, USA.
- Leonard, L., 2019. Using machine learning models to predict and choose meshes reordered by graph algorithms to improve execution times for hydrological modeling. Environmental Modelling & Software, 119, 84–98.
- Li, B. and Cheng, C., 2014. Monthly discharge forecasting using wavelet neural networks with extreme learning machine. Science China Technological Sciences, 57 (12), 2441–2452.
- Li, X., et al., 2017a. Comparison of hybrid models for daily streamflow prediction in a forested basin. Journal of Hydroinformatics, 20 (1), 191–205.
- Li, X., Sha, J., and Wang, Z.-L., 2017b. A comparative study of multiple linear regression, artificial neural network and support vector machine for the prediction of dissolved oxygen. Hydrology Research, 48 (5), 1214–1225.
- Li, X., Sha, J., and Wang, Z.-L., 2018. Application of feature selection and regression models for chlorophyll-a prediction in a shallow lake. Environmental Science and Pollution Research, 25 (20), 19488–19498.
- Liaw, A. and Wiener, M., 2002. Classification and regression by randomForest. R News, 2 (3), 18–22.
- Lima, A.R., Cannon, A.J., and Hsieh, W.W., 2015. Nonlinear regression in environmental sciences using extreme learning machines: a comparative evaluation. Environmental Modelling & Software, 73, 175–188.
- Lima, A.R., Cannon, A.J., and Hsieh, W.W., 2016. Forecasting daily streamflow using online sequential extreme learning machines. Journal of Hydrology, 537, 431–443.
- Lin, S.-W., et al., 2008. Particle swarm optimization for parameter determination and feature selection of support vector machines. Expert Systems with Applications, 35 (4), 1817–1824. doi:10.1016/j.eswa.2007.08.088
- Liu, X., Gao, C., and Li, P., 2012. A comparative analysis of support vector machines and extreme learning machines. Neural Networks, 33, 58–66.
- Ma, Z., Dai, Q., and Liu, N., 2015. Several novel evaluation measures for rank-based ensemble pruning with applications to time series prediction. Expert Systems with Applications, 42 (1), 280–292.
- Naghibi, S.A., Ahmadi, K., and Daneshi, A., 2017. Application of support vector machine, random forest, and genetic algorithm optimized random forest models in groundwater potential mapping. Water Resources Management, 31 (9), 2761–2775.
- Naghibi, S.A. and Pourghasemi, H.R., 2015. A comparative assessment between three machine learning models and their performance comparison by bivariate and multivariate statistical methods in groundwater potential mapping. Water Resources Management, 29 (14), 5217–5236.
- Oliveira, S., et al., 2012. Modeling spatial patterns of fire occurrence in mediterranean Europe using multiple regression and random forest. Forest Ecology and Management, 275, 117–129.
- PACKARD, N.H., et al., 1980. Geometry from a time series. Physical Review Letters, 45 (9), 712.
- Pond, G.J., 2012. Biodiversity loss in Appalachian headwater streams (Kentucky, USA): Plecoptera and Trichoptera communities. Hydrobiologia, 679 (1), 97–117.
- Singh, G., Panda, R.K., and Lamers, M., 2015. Modeling of daily runoff from a small agricultural watershed using artificial neural network with resampling techniques. Journal of Hydroinformatics, 17 (1), 56–74.
- Sivakumar, B., et al., 1999. Singapore rainfall behavior: chaotic? Journal of Hydrologic Engineering, 4 (1), 38–48.
- Song, G. and Dai, Q., 2017. A novel double deep ELMs ensemble system for time series forecasting. Knowledge-Based Systems, 134, 31–49.
- Sun, Y., Babovic, V., and Chan, E.S., 2010. Multi-step-ahead model error prediction using time-delay neural networks combined with chaos theory. Journal of Hydrology, 395 (1), 109–116.
- Takens, F., 1981. Detecting strange attractors in turbulence. Dynamical Systems and Turbulence, Warwick, 1980 (Springer), 366–381.
- Taormina, R. and Chau, K.-W., 2015a. Data-driven input variable selection for rainfall–runoff modeling using binary-coded particle swarm optimization and extreme learning machines. Journal of Hydrology, 529, 1617–1632.
- Taormina, R. and Chau, K.-W., 2015b. Neural network river forecasting with multi-objective fully informed particle swarm optimization. Journal of Hydroinformatics, 17 (1), 99–113.
- Vincenzi, S., et al., 2011. Application of a random forest algorithm to predict spatial distribution of the potential yield of Ruditapes philippinarum in the Venice lagoon, Italy. Ecological Modelling, 222 (8), 1471–1478.
- Were, K., et al., 2015. A comparative assessment of support vector regression, artificial neural networks, and random forests for predicting and mapping soil organic carbon stocks across an Afromontane landscape. Ecological Indicators, 52, 394–403.
- Worland, S.C., Farmer, W.H., and Kiang, J.E., 2018. Improving predictions of hydrological low-flow indices in ungaged basins using machine learning. Environmental Modelling & Software, 101, 169–182.
- Yaseen, Z.M., et al., 2016. Stream-flow forecasting using extreme learning machines: a case study in a semi-arid region in Iraq. Journal of Hydrology, 542, 603–614.
- Ye, R. and Dai, Q., 2018. A novel transfer learning framework for time series forecasting. Knowledge-Based Systems, 156, 74–99.
- Yu, P.-S., et al., 2017. Comparison of random forests and support vector machine for real-time radar-derived rainfall forecasting. Journal of Hydrology, 552, 92–104.
- Zhang, Y., et al., 2018. Multi-kernel extreme learning machine for EEG classification in brain-computer interfaces. Expert Systems with Applications, 96, 302–310.
- Zuo, D., et al., 2014. Identification of streamflow response to climate change and human activities in the Wei River Basin, China. Water Resources Management, 28 (3), 833–851.