Managing water quality in a river basin with uncertainty

The effects of both climate change and the geographic location of Taiwan have influenced the perceived variability of river flow and increased uncertainty and complexity in the management of river basins. In this study, a genetic algorithm (GA) optimizer was integrated into a stochastic river basin model to develop a stochastic optimization river basin management model (SORBMM). Firstly, the flow probability density function was determined through statistical analysis of the hydrological data. A Monte Carlo simulation was then conducted to evaluate the effect of flow variability, and a GA was implemented to obtain an optimal river pollution reduction strategy. A true case involving multi-objective management of a river basin under conditions of high spatiotemporal flow variation was tested to demonstrate the feasibility of the SORBMM. The results revealed that a reduction in pollution removal would lead to higher risks for river basin management due to the dilution effect in the river downstream and the objective of lowering pollution removal costs.

[1]  Martijn J. Booij,et al.  Uncertainty analysis in statistical modeling of extreme hydrological events , 2010 .

[2]  A. Alazba,et al.  Adaptation of climate variability/extreme in arid environment of the Arabian peninsula by rainwater harvesting and management , 2013, International Journal of Environmental Science and Technology.

[3]  C. A. G. Santos,et al.  Run-off–erosion modelling and water balance in the Epitácio Pessoa Dam river basin, Paraíba State in Brazil , 2018, International Journal of Environmental Science and Technology.

[4]  Harald Kunstmann,et al.  Direct propagation of probability density functions in hydrological equations , 2006 .

[5]  Hamed Ketabchi,et al.  Evolutionary algorithms for the optimal management of coastal groundwater: A comparative study toward future challenges , 2015 .

[6]  Hyunuk An,et al.  Ensemble urban flood simulation in comparison with laboratory-scale experiments: Impact of interaction models for manhole, sewer pipe, and surface flow , 2016 .

[7]  A. Danandeh Mehr,et al.  Successive-station monthly streamflow prediction using different artificial neural network algorithms , 2015, International Journal of Environmental Science and Technology.

[8]  D. Adrian,et al.  Comparative Evaluation of Three River Water Quality Models 1 , 2007 .

[9]  D G Altman,et al.  Confidence intervals for the number needed to treat , 1998, BMJ.

[10]  Seyed Mojtaba Sajadi,et al.  Multi-objective optimization of stochastic failure-prone manufacturing system with consideration of energy consumption and job sequences , 2018, International Journal of Environmental Science and Technology.

[11]  K. Sudheer,et al.  Quantification of the predictive uncertainty of artificial neural network based river flow forecast models , 2012, Stochastic Environmental Research and Risk Assessment.

[12]  Ni-Bin Chang,et al.  Using fuzzy operators to address the complexity in decision making of water resources redistribution in two neighboring river basins , 2010 .

[13]  D. Ames,et al.  Hydrologic impacts of climate and land-use change on Namnam Stream in Koycegiz Watershed, Turkey , 2015, International Journal of Environmental Science and Technology.

[14]  Guohe Huang,et al.  A risk-based interactive multi-stage stochastic programming approach for water resources planning under dual uncertainties , 2016 .

[15]  Guangtao Fu,et al.  Optimal Water Quality Management Considering Spatial and Temporal Variations in a Tidal River , 2013, Water resources management.

[16]  K. Havens,et al.  The importance of considering biological processes when setting total maximum daily loads (TMDL) for phosphorus in shallow lakes and reservoirs. , 2001, Environmental pollution.

[17]  Heinz G. Stefan,et al.  Stream flow in Minnesota : Indicator of climate change , 2007 .

[18]  Pan Liu,et al.  A Bayesian model averaging method for the derivation of reservoir operating rules , 2015 .

[19]  Bahram Gharabaghi,et al.  Estimating annual air emissions from nargyla water pipes in cafés and restaurants using Monte Carlo analysis , 2019, International Journal of Environmental Science and Technology.

[20]  G. Keppel,et al.  Design and Analysis: A Researcher's Handbook , 1976 .

[21]  Yi-Ping Chen,et al.  Multiobjective Optimal Design of Sewerage Rehabilitation by Using the Nondominated Sorting Genetic Algorithm-II , 2015, Water Resources Management.

[22]  D. Crevillén-García,et al.  Gaussian process modelling for uncertainty quantification in convectively-enhanced dissolution processes in porous media , 2017 .

[23]  Subhankar Karmakar,et al.  A two-phase grey fuzzy optimization approach for water quality management of a river system , 2007 .

[24]  Y. P. Li,et al.  Fuzzy-stochastic-based violation analysis method for planning water resources management systems with uncertain information , 2009, Inf. Sci..

[25]  Huaicheng Guo,et al.  Water quality modeling for load reduction under uncertainty: a Bayesian approach. , 2008, Water research.

[26]  E. Volpi,et al.  A simplified framework for assessing the impact of rainfall spatial variability on the hydrologic response , 2012 .

[27]  Yi-Ping Chen,et al.  Multiobjective optimization using nondominated sorting genetic algorithm-II for allocation of energy conservation and renewable energy facilities in a campus , 2016 .

[28]  A. Mejia,et al.  Anthropogenic controls from urban growth on flow regimes , 2015 .

[29]  N. Chang,et al.  Assessing pollution prevention program by QUAL2E simulation analysis for the Kao-Ping River Basin, Taiwan. , 2001, Journal of environmental management.

[30]  I. Muzik,et al.  A first-order analysis of the climate change effect on flood frequencies in a subalpine watershed by means of a hydrological rainfall-runoff model , 2002 .

[31]  Ni-Bin Chang,et al.  Water pollution control in the river basin by fuzzy genetic algorithm-based multiobjective programming modeling , 1998 .

[32]  Ming-Der Yang,et al.  Multiobjective optimization design of green building envelope material using a non-dominated sorting genetic algorithm , 2017 .

[33]  Konstantinos Liolios,et al.  Modeling of flow and BOD fate in horizontal subsurface flow constructed wetlands , 2012 .