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Zhong Li
Department of Civil Engineering McMaster University Hamilton ON Canada

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Research article
Published: 18 August 2021 in Water Resources Research
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The use of polynomial chaos expansion (PCE) has gained a lot of attention due to its ability to efficiently estimate the effects of parameter uncertainty on model outputs. The traditional PCE technique requires the studied parameters to be independent. In hydrological modeling, although model parameters are often assumed to be independent for simplicity of computation, such an assumption is not always valid. Neglecting parameter correlations could significantly affect the analysis of uncertainty, leading to distorted modeling results. In this study, an improved PCE approach is proposed to address this issue and support the uncertainty analysis for hydrological models with correlated parameters. The proposed approach is based on the integration of principle component analysis (PCA) and PCE, where PCA is used to transform correlated parameters into orthogonal independent components. To demonstrate the applicability of this approach, the SWAT model is applied to the Guadalupe River Watershed in Texas, US, and the integrated PCA-PCE framework is used to assess the propagation of uncertainty of SWAT interdependent parameters. A traditional Monte-Carlo (MC) simulation is also used to address the uncertainty in the developed SWAT model. The results show that PCA-PCE could generate similar probabilistic flow results compared to MC, while maintaining a very high computational efficiency. The coefficients of determination (R2) for the mean and variance are 0.998 and 0.973, respectively, and the computational requirement is reduced by 99% using the developed PCA-PCE approach. It is shown that the PCA-PCE approach is reliable and efficient in assessing uncertainties in hydrological models with correlated parameters.

ACS Style

Maysara Ghaith; Zhong Li; Brian W. Baetz. Uncertainty Analysis for Hydrological Models With Interdependent Parameters: An Improved Polynomial Chaos Expansion Approach. Water Resources Research 2021, 57, 1 .

AMA Style

Maysara Ghaith, Zhong Li, Brian W. Baetz. Uncertainty Analysis for Hydrological Models With Interdependent Parameters: An Improved Polynomial Chaos Expansion Approach. Water Resources Research. 2021; 57 (8):1.

Chicago/Turabian Style

Maysara Ghaith; Zhong Li; Brian W. Baetz. 2021. "Uncertainty Analysis for Hydrological Models With Interdependent Parameters: An Improved Polynomial Chaos Expansion Approach." Water Resources Research 57, no. 8: 1.

Journal article
Published: 01 August 2021 in Journal of Sustainable Water in the Built Environment
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For many jurisdictions, the current design criteria for low-impact development practices (LIDs) including infiltration trenches require LIDs to provide enough storage capacity to store catchment runoff from the location’s 90th-percentile storm. The 90th-percentile storm used in Ontario, Canada, has a depth of approximately 25 mm. This study examines the performances and costs of infiltration trenches built in Ontario but sized to accommodate alternative storm depths ranging from 5 to 50 mm. Analytical equations are used to determine the runoff reduction ratios of infiltration trenches, and a cost estimation tool specifically developed for LIDs is used to estimate their overall costs. Results indicate that the current 90th-percentile storm criterion is probably too high and not cost efficient. An evidence-based methodology for selecting more appropriate design criteria is proposed. Using this methodology, it was found that the economically more efficient design criterion for Ontario averages about 20–22 mm for different design cases. Significant savings can be realized if a lower design criterion is implemented. The proposed methodology is therefore recommended for jurisdictions seeking more cost-efficient design criteria.

ACS Style

Elizabeth Rowe; Yiping Guo; Zhong Li. Seeking More Cost-Efficient Design Criteria for Infiltration Trenches. Journal of Sustainable Water in the Built Environment 2021, 7, 04021009 .

AMA Style

Elizabeth Rowe, Yiping Guo, Zhong Li. Seeking More Cost-Efficient Design Criteria for Infiltration Trenches. Journal of Sustainable Water in the Built Environment. 2021; 7 (3):04021009.

Chicago/Turabian Style

Elizabeth Rowe; Yiping Guo; Zhong Li. 2021. "Seeking More Cost-Efficient Design Criteria for Infiltration Trenches." Journal of Sustainable Water in the Built Environment 7, no. 3: 04021009.

Journal article
Published: 02 June 2021 in Journal of Environmental Management
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Although integrated simulation-optimization modeling can provide a comprehensive and reliable analysis for water quality management (WQM), it is usually not easy to implement in practice. This study proposed a new efficient simulation-optimization modeling approach by leveraging the power of data-driven modeling, to support WQM under various uncertainties. A water quality simulation model is integrated with the optimization model, and then substituted by a series of numerical surrogate models based on inexact linear regression. The transformation can significantly reduce the computational burden and make it possible to implement uncertainty quantification through hybrid inexact programming. The proposed model incorporates interval quadratic programming and credibility constrained programming to deal with nonlinearity and various uncertainties associated with the management system. The proposed approach is applied to a real case study of the Grand River watershed in Canada for controlling phosphorus concentration in river water. The Grand River Simulation Model (GRSM) is employed as the physical simulation model to estimate the total phosphorus concentration in the river. Interval solutions under different confidence levels of violating the effluent standards were obtained, which can be used to generate optimal phosphorus control strategies. The results indicate the proposed data-driven interval credibility constrained quadratic programming (DICCQP) model is able to provide reliable and robust solutions for WQM by considering nonlinearity and various uncertainties while maintaining a high computational efficiency. The proposed new framework can be extended and applied to the other watersheds. The high efficiency of the proposed model makes it possible to solve large-scale complex water quality management and planning problems.

ACS Style

Qianqian Zhang; Zhong Li. Data-driven interval credibility constrained quadratic programming model for water quality management under uncertainty. Journal of Environmental Management 2021, 293, 112791 .

AMA Style

Qianqian Zhang, Zhong Li. Data-driven interval credibility constrained quadratic programming model for water quality management under uncertainty. Journal of Environmental Management. 2021; 293 ():112791.

Chicago/Turabian Style

Qianqian Zhang; Zhong Li. 2021. "Data-driven interval credibility constrained quadratic programming model for water quality management under uncertainty." Journal of Environmental Management 293, no. : 112791.

Journal article
Published: 08 May 2021 in Cold Regions Science and Technology
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The forecasting of river ice jams faces challenges relating to both the availability of data and the complexity of river ice dynamics, resulting in difficulties in model formulation. In this study, a hybrid ensemble modelling framework is developed to address the data scarcity issue and leverage advanced machine learning techniques for the prediction of ice jams with a one-day lead time. The proposed methodology utilises data easily monitored in advance of any ice jam events and maintains a realistic balance between ice jam and non-ice jam events. A combination of both single model algorithms, including classification trees, logistic regression, k-nearest neighbors, support vector machines, and artificial neural networks, and ensemble model algorithms, including random forest, adaptive boosting, gradient boosting, variance penalizing adaptive boosting, logistic boosting, class switching, and adaptive resampling and combining, are considered for both the member models of the first layer of the hybrid ensemble and for the ensemble combiner of the second layer. The final selection of both variables and member models for the hybrid ensemble is detailed, with a focus on the reduction of false negatives, the prediction of no ice jam on a day when one occurs. The proposed method is applied to the St. John River in New Brunswick, Canada, in a location particularly prone to ice jam flooding. Using the proposed methodology, a final model combining 6 different member models using a support vector machine as the ensemble combiner was produced, with a balanced prediction accuracy of 86%. This hybrid ensemble model outperformed the other tested ensemble models, as well as a series of generalized models produced using all available input variables and member models. The model also performed well against other ensemble techniques and against the individual member models. These results demonstrate the viability of the proposed methodology in constructing a hybrid ensemble model for the forecasting of ice jams on Northern Canadian Rivers. The techniques utilised can be adapted to other locations to facilitate ice jam forecasting, requiring data that is easily available and monitored in advance of any potential flooding events.

ACS Style

Michael De Coste; Zhong Li; Darryl Pupek; Wei Sun. A hybrid ensemble modelling framework for the prediction of breakup ice jams on Northern Canadian Rivers. Cold Regions Science and Technology 2021, 189, 103302 .

AMA Style

Michael De Coste, Zhong Li, Darryl Pupek, Wei Sun. A hybrid ensemble modelling framework for the prediction of breakup ice jams on Northern Canadian Rivers. Cold Regions Science and Technology. 2021; 189 ():103302.

Chicago/Turabian Style

Michael De Coste; Zhong Li; Darryl Pupek; Wei Sun. 2021. "A hybrid ensemble modelling framework for the prediction of breakup ice jams on Northern Canadian Rivers." Cold Regions Science and Technology 189, no. : 103302.

Journal article
Published: 01 May 2021 in Journal of Cleaner Production
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Agricultural water management has become an essential problem in recent years due to the increasing water demands. Irrigation water resources allocation is a dynamic decision making process associated with various uncertainties, which often exist in a complex and composite format. In this study, a new uncertainty quantification technique, the cloud model, is introduced to a dual-objective nonlinear programming (DONP) framework, and a cloud-based dual-objective nonlinear programming (CDONP) model is developed to support irrigation water allocation and agricultural water planning under composite uncertainties. The cloud model is applied to address the complex composite uncertainties associated with reference evapotranspiration (ET0) and surface water availability (SWA). A case study of the Yingke irrigation district (YID) in Northwest China is conducted to demonstrate the applicability of the developed model. The results show that the net economic profit (ENP) and irrigation system efficiency (ISE) are influenced by ET0 more than SWA. The obtained results are also compared to those of a traditional dual-objective nonlinear programming model to illustrate the advantages of the proposed CDONP model. In addition, four water shortage scenarios are built and discussed for risk analysis.

ACS Style

Zehao Yan; Brian Baetz; Zhong Li. A cloud-based dual-objective nonlinear programming model for irrigation water allocation in Northwest China. Journal of Cleaner Production 2021, 308, 127330 .

AMA Style

Zehao Yan, Brian Baetz, Zhong Li. A cloud-based dual-objective nonlinear programming model for irrigation water allocation in Northwest China. Journal of Cleaner Production. 2021; 308 ():127330.

Chicago/Turabian Style

Zehao Yan; Brian Baetz; Zhong Li. 2021. "A cloud-based dual-objective nonlinear programming model for irrigation water allocation in Northwest China." Journal of Cleaner Production 308, no. : 127330.

Article
Published: 13 October 2020
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The Yellow River Basin is of great significance to China's economic and social development and ecological security. The Yellow River Basin is not only an important ecological barrier but also an important economic zone. In this paper, natural hydrological conditions were taken as a reference, a habitat simulation model of the key sections of the Yellow River was constructed based on the MIKE 21 model, and an ecological water requirement assessment method for river ecological integrity combined with habitat simulation and features of the hydrological reference group was established, which takes into account the survival and reproduction of indicator species. The suitable flow rates for the spawning period of Silurus lanzhouensis in Lanzhou and Xiaheyan and Cyprinus carpio in Toudaoguai, Longmen and Huayuankou were 350-720 m3/s, 350-600 m3/s, 150-500 m³/s, 260-400 m3/s, and 100-500 m³/s, respectively. Therefore, high pulse flow with a low flow peak should be guaranteed in mid- to late April. The peak flow should be at least approximately 1,000 m3/s to ensure that fish receive spawning signals, with a high pulse flow process occurring 1-2 times in May to June. The annual ecological water requirement of the Lanzhou, Xiaheyan, Toudaoguai, Longmen and Huayuankou sections was 9.1-11 ×109 m³, 6.3-10.4×109 m³, 3.8-8.2×109 m³, 4.7-11.3×109 m³ and 7.9-15.4×109 m³, respectively. The model quantitatively simulates the changes in ecological water requirement of indicator fishes in key sections of the Yellow River, and an effective and more realistic tool for ecological water requirement accounting of the Yellow River has been provided.

ACS Style

Fen Zhao; Chunhui Li; Wenxiu Shang; Xiaokang Zheng; Zoe Li; Xuan Wang; Qiang Liu; Wanyu Ma; Jiuhe Bu; Yujun Yi. Ecological water requirement accounting of the main stream of the Yellow River from the perspective of habitat conservation. 2020, 1 .

AMA Style

Fen Zhao, Chunhui Li, Wenxiu Shang, Xiaokang Zheng, Zoe Li, Xuan Wang, Qiang Liu, Wanyu Ma, Jiuhe Bu, Yujun Yi. Ecological water requirement accounting of the main stream of the Yellow River from the perspective of habitat conservation. . 2020; ():1.

Chicago/Turabian Style

Fen Zhao; Chunhui Li; Wenxiu Shang; Xiaokang Zheng; Zoe Li; Xuan Wang; Qiang Liu; Wanyu Ma; Jiuhe Bu; Yujun Yi. 2020. "Ecological water requirement accounting of the main stream of the Yellow River from the perspective of habitat conservation." , no. : 1.

Journal article
Published: 18 September 2020 in Environmental Research
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Effective river water quality management and planning is a complex issue challenged by various complexities and uncertainties. A simulation-based interval chance-constrained quadratic programming (ICCQP) model is developed for the seasonal planning of water quality management (WQM) under various uncertainties. The proposed model incorporates interval quadratic programming, chance-constrained programming, and a seasonal water quality simulation model within a general framework for WQM. Uncertainties associated with the objective and the coefficients in the left-hand side of the constraints are tackled as intervals. Meanwhile, parameter uncertainties on the right-hand sides are characterized using probability distributions. Nonlinearities in the cost function are reflected by quadratic programming. A multi-segment water quality model is used to simulate the dynamic interactions between wastewater discharges and river water quality. The proposed ICCQP-WQM model is applied in a real case study for the control of total phosphorus (TP) in the central Grand River in Ontario, Canada. The results demonstrate that the proposed model is able to incorporate uncertainties expressed as intervals and probability information into an optimization framework and provide interval solutions. Thus, different cost-effective schemes for seasonal WQM could be generated. The results show the Kitchener wastewater treatment plant (WWTP) affects the value of the objective function more than the other WWTPs in the study area. It is also found that the Kitchener WWTP’s cost accounts for the highest proportion (approximately 35.1 -37.9%,) of the total annual cost, which implies the control of TP at the Kitchener plant is the most important to the system. Moreover, river water TP standards in spring and autumn are usually difficult to meet, indicating different TP control strategies are needed in these two seasons. The generated results are valuable for local decision-makers to generate TP control strategies, and also to identify optimized solutions under various uncertainties. The proposed ICCQP-WQM model can be extended to other watersheds to support effective water quality management and planning.

ACS Style

Qianqian Zhang; Zhong Li; Wendy Huang. Simulation-based interval chance-constrained quadratic programming model for water quality management: A case study of the central Grand River in Ontario, Canada. Environmental Research 2020, 192, 110206 .

AMA Style

Qianqian Zhang, Zhong Li, Wendy Huang. Simulation-based interval chance-constrained quadratic programming model for water quality management: A case study of the central Grand River in Ontario, Canada. Environmental Research. 2020; 192 ():110206.

Chicago/Turabian Style

Qianqian Zhang; Zhong Li; Wendy Huang. 2020. "Simulation-based interval chance-constrained quadratic programming model for water quality management: A case study of the central Grand River in Ontario, Canada." Environmental Research 192, no. : 110206.

Research article
Published: 17 September 2020 in Irrigation and Drainage
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Water scarcity causes conflicts between natural resources and socio‐economic development which reinforces the need for optimal allocation of irrigation water resources. Irrigation water resource allocation is a complex problem due to various uncertainties in natural conditions. In this study, a stochastic multi‐objective nonlinear programming model is developed for irrigation water allocation under uncertainty. The model is capable of balancing the conflicting objectives of maximizing both net economic benefit (NEB) and irrigation water use efficiency (IWUE). Moreover, it can reflect the random nature of water availability, and provide alternative water allocation schemes in response to climate change. The applicability of the developed model is demonstrated by a case study in north‐west China. Trade‐offs between NEB and IWUE are presented. Irrigation water allocation schemes to cope with changing environments, including climate change and varying water availability, are also proposed. The results demonstrate that the developed model can generate solutions that save irrigation water while ensuring NEB. This model is a useful tool to support the formulation of optimized water resources management policies in a changing environment. © 2020 John Wiley & Sons, Ltd.

ACS Style

Zehao Yan; Mo Li; Zhong Li. Efficient and Economical Allocation of Irrigation Water under a Changing Environment: a Stochastic Multi‐Objective Nonlinear Programming Model*. Irrigation and Drainage 2020, 70, 103 -116.

AMA Style

Zehao Yan, Mo Li, Zhong Li. Efficient and Economical Allocation of Irrigation Water under a Changing Environment: a Stochastic Multi‐Objective Nonlinear Programming Model*. Irrigation and Drainage. 2020; 70 (1):103-116.

Chicago/Turabian Style

Zehao Yan; Mo Li; Zhong Li. 2020. "Efficient and Economical Allocation of Irrigation Water under a Changing Environment: a Stochastic Multi‐Objective Nonlinear Programming Model*." Irrigation and Drainage 70, no. 1: 103-116.

Journal article
Published: 16 March 2020 in Journal of Hydrology
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Soil and Water Assessment Tool (SWAT) is one of the most widely used semi-distributed hydrological models. Assessment of the uncertainties in SWAT outputs is a popular but challenging topic due to the significant number of parameters. The purpose of this study is to investigate the use of Polynomial Chaos Expansion (PCE) in assessing uncertainty propagation in SWAT under the impact of significant parameter sensitivity. Furthermore, for the first time, a machine learning technique (i.e., artificial neural network, ANN) is integrated with PCE to expand its capability in generating probabilistic forecasts of daily flow. The traditional PCE and the proposed PCE-ANN methods are applied to a case study in the Guadalupe watershed in Texas, USA to assess the uncertainty propagation in SWAT for flow prediction during the historical and forecasting periods. The results show that PCE provides similar results as the traditional Monte-Carlo (MC) method, with a coefficient of determination (R2) value of 0.99 for the mean flow, during the historical period; while the proposed PCE-ANN method reproduces MC output with a R2 value of 0.84 for mean flow during the forecasting period. It is also indicated that PCE and PCE-ANN are as reliable as but much more efficient than MC. PCE takes about 1% of the computational time required by MC; PCE-ANN only takes a few minutes to produce probabilistic forecasting, while MC requires running the model for dozens or hundreds, even thousands, of times. Notably, the development of the PCE-ANN framework is the first attempt to explore PCE’s probabilistic forecasting capability using machine learning. PCE-ANN is a promising uncertainty assessment and probabilistic forecasting technique, as it is more efficient in terms of computation time, and it does not cause loss of essential uncertainty information.

ACS Style

Maysara Ghaith; Zhong Li. Propagation of parameter uncertainty in SWAT: A probabilistic forecasting method based on polynomial chaos expansion and machine learning. Journal of Hydrology 2020, 586, 124854 .

AMA Style

Maysara Ghaith, Zhong Li. Propagation of parameter uncertainty in SWAT: A probabilistic forecasting method based on polynomial chaos expansion and machine learning. Journal of Hydrology. 2020; 586 ():124854.

Chicago/Turabian Style

Maysara Ghaith; Zhong Li. 2020. "Propagation of parameter uncertainty in SWAT: A probabilistic forecasting method based on polynomial chaos expansion and machine learning." Journal of Hydrology 586, no. : 124854.

Journal article
Published: 01 February 2020 in Journal of Hydrologic Engineering
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Hydrological forecasting is key for water resources allocation and flood risk management. Although a number of advanced hydrological forecasting methods have been developed in the past, daily (or subdaily) forecasting remains a major challenge in engineering hydrology. The uncertainties associated with input data, model parameters, and model structure necessitate developing more robust modeling techniques. In this study, a hybrid machine-learning approach based on hydrological and data-driven modeling is developed for daily streamflow forecasting. The proposed hybrid hydrological data-driven model (HHDD) approach succeeds in improving daily prediction compared to that predicted by the standard conceptual hydrological model (HYMOD). In addition, the developed HHDD model is more robust in terms of providing direct uncertainty analysis results. The results indicate that a better resemblance of streamflow pattern is achieved by integrating physically based and data-driven approaches into the developed HHDD model.

ACS Style

Maysara Ghaith; Ahmad Siam; Zhong Li; Wael El-Dakhakhni. Hybrid Hydrological Data-Driven Approach for Daily Streamflow Forecasting. Journal of Hydrologic Engineering 2020, 25, 04019063 .

AMA Style

Maysara Ghaith, Ahmad Siam, Zhong Li, Wael El-Dakhakhni. Hybrid Hydrological Data-Driven Approach for Daily Streamflow Forecasting. Journal of Hydrologic Engineering. 2020; 25 (2):04019063.

Chicago/Turabian Style

Maysara Ghaith; Ahmad Siam; Zhong Li; Wael El-Dakhakhni. 2020. "Hybrid Hydrological Data-Driven Approach for Daily Streamflow Forecasting." Journal of Hydrologic Engineering 25, no. 2: 04019063.

Original paper
Published: 29 January 2020 in Theoretical and Applied Climatology
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Temperature changes have widespread impacts on the environment, economy, and municipal planning. Generating accurate climate prediction at finer spatial resolution through downscaling could help better assess the future effects of climate change on a local scale. Ensembles of multiple climate models have been proven to improve the accuracy of temperature prediction. Meanwhile, machine learning techniques have shown high performance in solving various predictive modeling problems, which make them a promising tool for temperature downscaling. This study investigated the performance of machine learning (long short-term memory (LSTM) networks and support vector machine (SVM)) and statistical (arithmetic ensemble mean (EM) and multiple linear regression (MLR)) methods in developing multi-model ensembles for downscaling long-term daily temperature. A case study of twelve meteorological stations across Ontario, Canada, was conducted to evaluate the performance of the proposed ensembles. The results showed that both machine learning and statistical techniques performed well at downscaling daily temperature with multi-model ensembles and had similar performance with relatively high accuracy. The R2 of 12 stations ranged between 0.756 and 0.820 and RMSE ranged between 4.318 and 7.063 °C. Both machine learning and statistical ensembles for downscaling had difficulty in predicting extreme values for temperature below − 10 °C and above 20 °C. The results provided technical support for using statistical and machine learning methods to generate high-resolution daily temperature prediction.

ACS Style

Xinyi Li; Zhong Li; Wendy Huang; Pengxiao Zhou. Performance of statistical and machine learning ensembles for daily temperature downscaling. Theoretical and Applied Climatology 2020, 140, 571 -588.

AMA Style

Xinyi Li, Zhong Li, Wendy Huang, Pengxiao Zhou. Performance of statistical and machine learning ensembles for daily temperature downscaling. Theoretical and Applied Climatology. 2020; 140 (1-2):571-588.

Chicago/Turabian Style

Xinyi Li; Zhong Li; Wendy Huang; Pengxiao Zhou. 2020. "Performance of statistical and machine learning ensembles for daily temperature downscaling." Theoretical and Applied Climatology 140, no. 1-2: 571-588.

Journal article
Published: 09 January 2020 in Journal of Hydrology
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Establishing an optimal water allocation scheme is a challenging yet crucial task for effective risk management of water resources. The dynamic and complex interaction among various risk-related components in a water resources management system cannot be easily modeled to characterize the system response under different risk control settings. In this study, a risk-based fuzzy boundary interval two-stage stochastic water resources management programming (RFITSWMP) model is developed for the optimization of water consumption. This model incorporates a series of risk control constraints, such as water availability, maximum allowable penalty, and allowable benefit violation constraints into a fuzzy boundary interval two-stage stochastic programming framework for water resources management. It can address the uncertainties presented as fuzzy boundary intervals and probability distributions. It can also tackle the recourse action to minimize penalties based on interactive influences of different risk control measures, further generating optimal water allocation alternatives and guiding water resources management. This developed model is applied to a case study of water consumption optimization in the middle reaches of the Heihe River Basin in China. Feasible water allocation schemes under given risk levels, as well as the associated economic benefit, actual benefit and penalty loss are generated. The results can help decision makers to gain an insight into the inherent conflicts and tradeoffs amid risk, benefit and water allocation. The performance of developed model is further demonstrated by comparing it with a fuzzy boundary interval two stage stochastic water resources management programming (FITSWMP) model. In addition, a multiple factorial analysis (MFA) approach is employed to analyze the impacts of interactive risk parameters on the optimal decisions. The result disclosed that (1) the higher individual risk-parameter levels correspond to higher water shortage, penalty loss and benefit value-at-risk but higher economic benefit. (2) The combination of risk parameters can achieve robust water allocation and economic benefit as the constraints concerned with risk parameters affect and limited by each other. (3) The condition that water violated probability (p) is 0.2, penalty violated risk level (β) is 1 and benefit violated risk level (γ) is 1 has the highest water allocation and biggest economic benefit.

ACS Style

Youzhi Wang; Zhong Li; Shanshan Guo; Fan Zhang; Ping Guo. A risk-based fuzzy boundary interval two-stage stochastic water resources management programming approach under uncertainty. Journal of Hydrology 2020, 582, 124553 .

AMA Style

Youzhi Wang, Zhong Li, Shanshan Guo, Fan Zhang, Ping Guo. A risk-based fuzzy boundary interval two-stage stochastic water resources management programming approach under uncertainty. Journal of Hydrology. 2020; 582 ():124553.

Chicago/Turabian Style

Youzhi Wang; Zhong Li; Shanshan Guo; Fan Zhang; Ping Guo. 2020. "A risk-based fuzzy boundary interval two-stage stochastic water resources management programming approach under uncertainty." Journal of Hydrology 582, no. : 124553.

Journal article
Published: 14 November 2019 in Journal of Cleaner Production
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Interval quadratic programming (IQP) is one of the most popular programming techniques for addressing the uncertainties associated with nonlinear environmental management problems. However, efficient algorithms for solving IQP problems in water quality management (WQM) have not been well studied. In this study, an IQP model is developed for WQM under uncertainty. Three solution algorithms, including a piecewise linear approximation (PLA) method, a derivative algorithm (DEA) and a duality-based algorithm (DUA) are proposed for solving the IQP-WQM problem. The developed model and the corresponding solution algorithms are applied to a hypothetic WQM problem to demonstrate their applicability. The results show the lower bounds of the total cost obtained by three algorithms have a relationship of fDUA−=fDEA−≤fPLA−, while that of the upper bounds is fDEA+≤fPLA+≤fDUA+. Moreover, the sensitivity analysis shows that no matter how the IQP-WQM model is solved, the model response from the three solution algorithms is consistent. The results indicate that all of the three algorithms can efficiently deal with quadratic programming problems under uncertainties in the format of intervals. Comparison among the three algorithms shows that DUA provides interval solutions with wider ranges than the other two methods, and it requires less computational efforts than DEA. It is also found that PLA is more flexible and might require lower computational efforts for large scale problems. This study can provide useful technical support for effective WQM.

ACS Style

Qianqian Zhang; Zhong Li. Development of an interval quadratic programming water quality management model and its solution algorithms. Journal of Cleaner Production 2019, 249, 119319 .

AMA Style

Qianqian Zhang, Zhong Li. Development of an interval quadratic programming water quality management model and its solution algorithms. Journal of Cleaner Production. 2019; 249 ():119319.

Chicago/Turabian Style

Qianqian Zhang; Zhong Li. 2019. "Development of an interval quadratic programming water quality management model and its solution algorithms." Journal of Cleaner Production 249, no. : 119319.

Original paper
Published: 20 September 2019 in Stochastic Environmental Research and Risk Assessment
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Influent flow of wastewater treatment plants (WWTPs) is a crucial variable for plant operation and management. In this study, a random forest (RF) model was applied for daily wastewater inflow prediction, and a new probabilistic prediction approach was, for the first time, applied for quantifying the uncertainties associated with wastewater inflow prediction. The RF model uses regression trees to capture the nonlinear relationship between wastewater inflow and various influencing factors, such as weather features and domestic water usage patterns. The proposed model was applied to the daily wastewater inflow prediction for two WWTPs (i.e., Humber and one confidential plant) in Ontario, Canada. For the confidential WWTP, the coefficient of determination (\(\varvec{R}^{2}\)) values for training and testing were 0.971 and 0.722, respectively. The \(\varvec{R}^{2}\) values at the Humber WWTP were 0.957 and 0.584 for training and testing, respectively. In comparison with other approaches such as the multilayer perceptron neural networks (MLP) models and autoregressive integrated moving average models, the results show that the RF model performs well on predicting inflow. In addition, probabilistic prediction of daily inflow was generated. For the Humber station, 93.56% of the total testing samples fall into its corresponding predicted interval. For the confidential plant, 78 observed values of the total 89 samples fall into its corresponding interval, accounting for 87.64% of the total testing samples. The results show that the probabilistic approach can provide robust decision support for the operation, management, and optimization of WWTPs.

ACS Style

Pengxiao Zhou; Zhong Li; Spencer Snowling; Brian W. Baetz; Dain Na; Gavin Boyd. A random forest model for inflow prediction at wastewater treatment plants. Stochastic Environmental Research and Risk Assessment 2019, 33, 1781 -1792.

AMA Style

Pengxiao Zhou, Zhong Li, Spencer Snowling, Brian W. Baetz, Dain Na, Gavin Boyd. A random forest model for inflow prediction at wastewater treatment plants. Stochastic Environmental Research and Risk Assessment. 2019; 33 (10):1781-1792.

Chicago/Turabian Style

Pengxiao Zhou; Zhong Li; Spencer Snowling; Brian W. Baetz; Dain Na; Gavin Boyd. 2019. "A random forest model for inflow prediction at wastewater treatment plants." Stochastic Environmental Research and Risk Assessment 33, no. 10: 1781-1792.

Journal article
Published: 15 July 2019 in Water Science and Technology
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Wastewater flow forecasting is key for proper management of wastewater treatment plants (WWTPs). However, to predict the amount of incoming wastewater in WWTPs, wastewater engineers face challenges arising from numerous complexities and uncertainties, such as the nonlinear precipitation-runoff relationships in combined sewer systems, unpredictability due to aging infrastructure, and frequently inconsistent data quality. To address such challenges, a time series analysis model (i.e., the autoregressive integrated moving average, ARIMA) and an artificial neural network model (i.e., the multilayer perceptron neural network, MLPNN) were developed for predicting wastewater inflow. A case study of the Barrie Wastewater Treatment Facility in Barrie, Canada, was carried out to demonstrate the performance of the proposed models. Fifteen-minute flow data over a period of 1 year were collected, and the resampled daily flow data were used to train and validate the developed models. The model performances were examined using root mean square error, mean absolute percentage error, coefficient of determination, and Nash–Sutcliffe efficiency. The results indicate that both models provided reliable forecasts, while ARIMA showed a slightly better performance than MLPNN in this case study. The proposed models can provide useful decision support for the optimization and management of WWTPs.

ACS Style

Qianqian Zhang; Zhong Li; Spencer Snowling; Ahmad Siam; Wael El-Dakhakhni. Predictive models for wastewater flow forecasting based on time series analysis and artificial neural network. Water Science and Technology 2019, 80, 243 -253.

AMA Style

Qianqian Zhang, Zhong Li, Spencer Snowling, Ahmad Siam, Wael El-Dakhakhni. Predictive models for wastewater flow forecasting based on time series analysis and artificial neural network. Water Science and Technology. 2019; 80 (2):243-253.

Chicago/Turabian Style

Qianqian Zhang; Zhong Li; Spencer Snowling; Ahmad Siam; Wael El-Dakhakhni. 2019. "Predictive models for wastewater flow forecasting based on time series analysis and artificial neural network." Water Science and Technology 80, no. 2: 243-253.

Journal article
Published: 10 April 2019 in Sustainability
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Different from the traditional irrigation optimization model based only on the water production function, in this study, we explored the water–yield–quality–benefit relationship and established a general irrigation scheduling optimization framework. To establish the framework, (1) an artificial neural network coupled with ensemble empirical mode decomposition (EEMD-ANN) is used to decompose the original price time series into several subseries and then forecast each of them; (2) factor analysis and a technique for order of preference by similarity to ideal solution (FA-TOPSIS), as an integrated evaluation method, is used to comprehensively evaluate the fruit quality parameters; and (3) regression analysis is used to simulate water-yield and water-fruit quality relationships. The model is applied to a case study of greenhouse tomato irrigation schedule optimization. The results indicate that EEMD-ANN can improve the accuracy of price forecasting. Jensen and additive models are selected to simulate the relationships of tomato yield and quality with water deficit at various stages. Besides, the model can balance the contradiction between higher yields and better quality, and optimal irrigation scheduling is obtained under different market conditions. Comparison between the developed model and a traditional modeling approach indicates that the former can improve net benefits, fruit quality, and water use efficiency. This model considers the economic mechanism of market price changing with fruit quality. Forecasting and optimization results can provide reliable and useful advices for local farmers on planting and irrigation.

ACS Style

Baoying Shan; Ping Guo; Shanshan Guo; Zhong Li. A Price-Forecast-Based Irrigation Scheduling Optimization Model under the Response of Fruit Quality and Price to Water. Sustainability 2019, 11, 2124 .

AMA Style

Baoying Shan, Ping Guo, Shanshan Guo, Zhong Li. A Price-Forecast-Based Irrigation Scheduling Optimization Model under the Response of Fruit Quality and Price to Water. Sustainability. 2019; 11 (7):2124.

Chicago/Turabian Style

Baoying Shan; Ping Guo; Shanshan Guo; Zhong Li. 2019. "A Price-Forecast-Based Irrigation Scheduling Optimization Model under the Response of Fruit Quality and Price to Water." Sustainability 11, no. 7: 2124.

Journal article
Published: 23 March 2019 in Sustainability
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Autoregressive Integrated Moving Average (ARIMA) is a time series analysis model that can be dated back to 1955. It has been used in many different fields of study to analyze time series and forecast future data points; however, it has not been widely used to forecast daily wastewater influent flow. The objective of this study is to explore the possibility for wastewater treatment plants (WWTPs) to utilize ARIMA for daily influent flow forecasting. To pursue the objective confidently, five stations across North America are used to validate ARIMA’s performance. These stations include Woodward, Niagara, North Davis, and two confidential plants. The results demonstrate that ARIMA models can produce satisfactory daily influent flow forecasts. Considering the results of this study, ARIMA models could provide the operating engineers at both municipal and rural WWTPs with sufficient information to run the stations efficiently and thus, support wastewater management and planning at various levels within a watershed.

ACS Style

Gavin Boyd; Dain Na; Zhong Li; Spencer Snowling; Qianqian Zhang; Pengxiao Zhou. Influent Forecasting for Wastewater Treatment Plants in North America. Sustainability 2019, 11, 1764 .

AMA Style

Gavin Boyd, Dain Na, Zhong Li, Spencer Snowling, Qianqian Zhang, Pengxiao Zhou. Influent Forecasting for Wastewater Treatment Plants in North America. Sustainability. 2019; 11 (6):1764.

Chicago/Turabian Style

Gavin Boyd; Dain Na; Zhong Li; Spencer Snowling; Qianqian Zhang; Pengxiao Zhou. 2019. "Influent Forecasting for Wastewater Treatment Plants in North America." Sustainability 11, no. 6: 1764.

Journal article
Published: 01 February 2019 in Applied Energy
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ACS Style

X.T. Zeng; J.L. Zhang; L. Yu; Jinxin Zhu; Z. Li; L. Tang. A sustainable water-food-energy plan to confront climatic and socioeconomic changes using simulation-optimization approach. Applied Energy 2019, 236, 743 -759.

AMA Style

X.T. Zeng, J.L. Zhang, L. Yu, Jinxin Zhu, Z. Li, L. Tang. A sustainable water-food-energy plan to confront climatic and socioeconomic changes using simulation-optimization approach. Applied Energy. 2019; 236 ():743-759.

Chicago/Turabian Style

X.T. Zeng; J.L. Zhang; L. Yu; Jinxin Zhu; Z. Li; L. Tang. 2019. "A sustainable water-food-energy plan to confront climatic and socioeconomic changes using simulation-optimization approach." Applied Energy 236, no. : 743-759.

Journal article
Published: 01 January 2019 in Journal of Environmental Informatics Letters
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ACS Style

P. Zhou; Zhong Li; S. Snowling; R. Goel; Q. Zhang; Inc. Hydromantis Environmental Software Solutions. Short-Term Wastewater Influent Prediction Based on Random Forests and Multi-Layer Perceptron. Journal of Environmental Informatics Letters 2019, 1 .

AMA Style

P. Zhou, Zhong Li, S. Snowling, R. Goel, Q. Zhang, Inc. Hydromantis Environmental Software Solutions. Short-Term Wastewater Influent Prediction Based on Random Forests and Multi-Layer Perceptron. Journal of Environmental Informatics Letters. 2019; ():1.

Chicago/Turabian Style

P. Zhou; Zhong Li; S. Snowling; R. Goel; Q. Zhang; Inc. Hydromantis Environmental Software Solutions. 2019. "Short-Term Wastewater Influent Prediction Based on Random Forests and Multi-Layer Perceptron." Journal of Environmental Informatics Letters , no. : 1.

Journal article
Published: 01 January 2019 in Journal of Environmental Informatics Letters
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Temperature is one of the most important parameters in climate modeling, as it has significant impacts on various geophysical processes such as evaporation and precipitation. Applying multiple climate models for prediction generally outperforms the use of individual climate models, and neural networks perform well at capturing nonlinear relationships, which can provide more reliable temperature projections. In this study, three neural network algorithms, including Multi-layer Perceptron (MLP), Time-lagged Feed-forward Neural Networks (TLFN) and Nonlinear Auto-Regressive Networks with exogenous inputs (NARX), were used to develop data-driven models for predicting daily mean near-surface temperature based on North American Coordinated Regional Downscaling Experiment (NA-CORDEX) output. A case study of Big Trout Lake in Ontario, Canada was carried out to demonstrate the applications and to evaluate the performance of the proposed neural network based models. The results showed that MLP, TLFN, and NARX performed well in generating accurate daily near-surface temperature predictions with the coefficient of determination (R2) values above 0.84. The three neural network based models had similar performance with no significant difference in terms of root mean square error and R2. Neural network based climate prediction models outperformed each of the individual regional climate models and generated smoother predictions with less fluctuation. This study provides a technical basis for generating reliable predictions of daily temperature using neural networks based model.

ACS Style

X. Y. Li; Zhong Li; Q. Q. Zhang; P. X. Zhou; W. Huang. Prediction of Long-Term Near-Surface Temperature Based on NA-CORDEX Output. Journal of Environmental Informatics Letters 2019, 2, 10-18 .

AMA Style

X. Y. Li, Zhong Li, Q. Q. Zhang, P. X. Zhou, W. Huang. Prediction of Long-Term Near-Surface Temperature Based on NA-CORDEX Output. Journal of Environmental Informatics Letters. 2019; 2 (1):10-18.

Chicago/Turabian Style

X. Y. Li; Zhong Li; Q. Q. Zhang; P. X. Zhou; W. Huang. 2019. "Prediction of Long-Term Near-Surface Temperature Based on NA-CORDEX Output." Journal of Environmental Informatics Letters 2, no. 1: 10-18.