Abstract
Regression analysis aims to study the relationship between one variable, usually called the dependent variable, and several other variables, often called the independent variables. These models are among the most popular data-driven models for their easy application and very well-known techniques. Regression models range from linear to nonlinear and parametric to nonparametric models. In the field of water resources and environmental engineering, regression analysis is widely used for prediction, forecasting, estimation of missing data, and, in general, interpolation and extrapolation of data. This chapter presents models for point and interval estimation of dependent variables using different regression methods. Multiple linear regression model, conventional nonlinear regression models, K-nearest neighbor nonparametric model, and logistic regression model are presented in different sections of this chapter. Each model is supported by related commands and programs provided in MATLAB.
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References
Abaurrea J, Asin J, Cebrian AC, Garcia-Vera MA (2011) Trend analysis of water quality series based on regression models with correlated errors. J Hydrol 400:341–352
Adamowski J, Chan HF, Prasher SO, Ozga-Zielinski B, Sliusarieva A (2012) Comparison of multiple linear and nonlinear regression, autoregressive integrated moving average, artificial neural network, and wavelet artificial neural network methods for urban water demand forecasting in Montreal, Canada. Water Resour Res 48:W01528 (14)
Adeloye AJ, Lallemand F, McMahon TA (2003) Regression models for within-year capacity adjustment in reservoir planning. Hydrol Sci J 48(4):539–552
Cooley RL, Christensen S (2006) Bias and uncertainty in regression-calibrated models of groundwater flow in heterogeneous media. Adv Water Res 29:639–656
Crawford CG (1991) Estimation of suspended-sediment rating curves and mean suspended-sediment loads. J Hydrol 129:331–348
Demetracopoulo AC (1994) Nonlinear regression applied to hydrologic data. J Irrig Drain Eng 120(3):652–659
Gangopadhyay S, Clark M, Rajagopalan B (2005) Statistical downscaling using K-nearest neighbors. Water Resour Res 41:W02024 (23)
Hu J, Liu J, Liu Y, Gao C (2011) EMD-KNN model for annual average rainfall forecasting. J Hydrol Eng. doi:10.1061/(ASCE)HE.1943-5584.0000481
Karlsson M, Yakowitz S (1987) Nearest-neighbor methods for nonparametric rainfall-runoff forecasting. Water Resour Res 23(7):1300–1308
Lall U, Sharma A (1996) A nearest neighbor bootstrap for resampling hydrologic time series. Water Resour Res 32(3):679–694
Mahalanobis PC (1936) On the generalised distance in statistics. Proc Natl Inst Sci India 2(1):49–55
Mehrotra R, Sharma A (2006) Conditional resampling of hydrologic time series using multiple predictor variables: a K-nearest neighbour approach. Adv Water Res 29:987–999
Muluye GY (2011) Implications of medium-range numerical weather model output in hydrologic applications: assessment of skill and economic value. J Hydrol 400:448–464
Ozdemir A (2011) Using a binary logistic regression method and GIS for evaluating and mapping the groundwater spring potential in the Sultan Mountains (Aksehir, Turkey). J Hydrol 405:123–136
Petersen-Øverleir A (2006) Modelling stage-discharge relationships affected by hysteresis using the Jones formula and nonlinear regression. Hydrol Sci J 51(3):365–388
Qian SS, Reckhow KH, Zhai J, McMahon G (2005) Nonlinear regression modeling of nutrient loads in streams: a Bayesian approach. Water Resour Res 41:W07012 (10)
Rajagopalan B, Lall U (1999) A k–nearest-neighbor simulator for daily precipitation and other weather variables. Water Resour Res 39(10):3089–3101
Regonda SK, Rajagopalan B, Clark M (2006) A new method to produce categorical streamflow forecasts. Water Resour Res 42:W09501 (6)
Sokol Z (2003) Utilization of regression models for rainfall estimates using radar-derived rainfall data and rain gauge data. J Hydrol 278:144–152
Tran HD, Perera JC, Ng AWM (2009) Predicting structural deterioration condition of individual storm-water pipes using probabilistic neural networks and multiple logistic regression models. J Water Resour Plann Manage 135(6):553–557
Yates D, Gangopadhyay S, Rajagopalan B, Strzepek K (2003) A technique for generating regional climate scenarios using a nearest-neighbor algorithm. Water Resour Res 39(7):1114–1121
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Araghinejad, S. (2014). Regression-Based Models. In: Data-Driven Modeling: Using MATLAB® in Water Resources and Environmental Engineering. Water Science and Technology Library, vol 67. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-7506-0_3
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DOI: https://doi.org/10.1007/978-94-007-7506-0_3
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