Yuankun Wang

A cloud model-based approach for water quality assessment

Water quality assessment entails essentially a multi-criteria decision-making process accounting for qualitative and quantitative uncertainties and their transformation. Considering uncertainties of randomness and fuzziness in water quality evaluation, a cloud model-based assessment approach is proposed. The cognitive cloud model, derived from information science, can realize the transformation between qualitative concept and quantitative data, based on probability and statistics and fuzzy set theory. When applying the cloud model to practical assessment, three technical issues are considered before the development of a complete cloud model-based approach: (1) bilateral boundary formula with nonlinear boundary regression for parameter estimation, (2) hybrid entropy-analytic hierarchy process technique for calculation of weights, and (3) mean of repeated simulations for determining the degree of final certainty. The cloud model-based approach is tested by evaluating the eutrophication status of 12 typical lakes and reservoirs in China and comparing with other four methods, which are Scoring Index method, Variable Fuzzy Sets method, Hybrid Fuzzy and Optimal model, and Neural Networks method. The proposed approach yields information concerning membership for each water quality status which leads to the final status. The approach is found to be representative of other alternative methods and accurate.

Variable Fuzzy Set Theory to Assess Water Quality of the Meiliang Bay in Taihu Lake Basin

The water quality assessment is a fuzzy concept with multiple indicators and classes. Because of subjectivity in determining index weight and assessment standard as point forms in water quality assessment, evaluation results are often incompatible and independent, and sometimes with unreliable conclusions. An assessment model is developed, based on the variable fuzzy set and the information entropy theory. The model is applied to assess the water quality status of the Meiliang Bay of the Taihu Lake in China. Results show that the proposed model can determine the water quality level and provide an acceptable alternative based on optimized objectivity in determining water quality level. This study could provide a scientific basis for analyzing and evaluating the water quality for environment management.

A multidimension cloud model-based approach for water quality assessment

Lakes are vitally important, because they perform a multitude of functions, such as water supply, recreation, fishing, and habitat. However, eutrophication limits the ability of lakes to perform these functions. In order to reduce eutrophication, the first step is its evaluation. The process of evaluation entails randomness and fuzziness which must therefore be incorporated. This study proposes an eutrophication evaluation method, named Multidimension Normal Cloud Model (MNCM). The model regards each evaluation factor as a one-dimension attribute of MNCM, chooses reasonable parameters and determines the weights of evaluation factors by entropy. Thus, all factors of MNCM belonging to each eutrophication level are generated and the final eutrophication level is determined by the certainty degree. MNCM is then used to evaluate eutrophication of 12 typical lakes and reservoirs in China and its results are compared with those of the reference method, one-dimension normal cloud model, related weighted nutrition state index method, scoring method, and fuzzy comprehensive evaluation method. Results of MNCM are found to be consistent with the actual water status; hence, MNCM can be an effective evaluation tool. With respect to the former one-dimension normal cloud model, parameters of MNCM are improved without increasing its complexity. MNCM can directly determine the eutrophication level according to the degree of certainty and can determine the final degree of eutrophication; thus, it is more consistent with the complexity of water eutrophication evaluation.

Non-Carcinogenic Baseline Risk Assessment of Heavy Metals in the Taihu Lake Basin, China

ABSTRACT The aim of this study was to investigate the contamination levels of heavy metals and to develop the baseline risk to health effects within the source areas of drinking water in Taihu Lake Basin (TLB), an area undergoing population and economic growth. Samples of water were collected from 12 representative drinking water supply areas of TLB, and were analyzed for seven heavy metals. The Hazard Indexes (HIs) for all oral exposure to the seven metals ranged from 0.03 to 0.08, which was less than the acceptable non-cancer human health hazard index of 1. As and Fe together contributed more than 98% of the overall HI, which suggests that future monitoring of water quality for these two metals should be considered.

A risk assessment method based on RBF artificial neural network-cloud model for urban water hazard

A risk assessment for urban water hazard based on RBF artificial neural network-Cloud model (RBF-ANN-Cloud) is proposed, according to the nonlinear characteristics, randomness and fuzziness in water hazard. Four assessment factors influencing urban water hazard are selected; the ranges of risk levels are calculated according to the Pearson-III frequency curve and the comprehensive cloud model of all risk levels belonging to assessment factors are generated. Historical data of assessment factors are simulated and forecasted by RBF artificial neural network; distribution curves of certainty degrees of risk levels are drawn, which indicate the final water hazard risk. Comparative researches with ARIMA and fuzzy decision-making set showed RBF-ANN-Clouds suitability and effectiveness in water hazard risk assessment. RBF-Cloud model provides a new way of forecast and assessment of urban water hazard.

A Variable Fuzzy Set Assessment Model for Water Shortage Risk: Two Case Studies from China

ABSTRACT Water shortage risk assessment is full of uncertainty. Hence a water shortage assessment model was developed based on the variable fuzzy set, which reasonably identifies the relative membership degree and function of simple index and standard interval of each level. The model is used to assess water shortage risks in the Yangtze River Delta (which comprises the cities of Shanghai and Nanjing) and in the Capital-area of China (including the cities of Beijing and Tianjin). Results show that the proposed model rationally determines the membership grade in the range of rankings for all related indicators. More importantly, the proposed model is flexible and adaptable for diagnosing water shortage risks for different ecological conditions.

A Hybrid Wavelet de-noising and Rank-Set Pair Analysis approach for forecasting hydro-meteorological time series

Abstract Accurate, fast forecasting of hydro‐meteorological time series is presently a major challenge in drought and flood mitigation. This paper proposes a hybrid approach, wavelet de‐noising (WD) and Rank‐Set Pair Analysis (RSPA), that takes full advantage of a combination of the two approaches to improve forecasts of hydro‐meteorological time series. WD allows decomposition and reconstruction of a time series by the wavelet transform, and hence separation of the noise from the original series. RSPA, a more reliable and efficient version of Set Pair Analysis, is integrated with WD to form the hybrid WD‐RSPA approach. Two types of hydro‐meteorological data sets with different characteristics and different levels of human influences at some representative stations are used to illustrate the WD‐RSPA approach. The approach is also compared to three other generic methods: the conventional Auto Regressive Integrated Moving Average (ARIMA) method, Artificial Neural Networks (ANNs) (BP‐error Back Propagation, MLP‐Multilayer Perceptron and RBF‐Radial Basis Function), and RSPA alone. Nine error metrics are used to evaluate the model performance. Compared to three other generic methods, the results generated by WD‐REPA model presented invariably smaller error measures which means the forecasting capability of the WD‐REPA model is better than other models. The results show that WD‐RSPA is accurate, feasible, and effective. In particular, WD‐RSPA is found to be the best among the various generic methods compared in this paper, even when the extreme events are included within a time series. HighlightsA new hybrid approach is proposed to improve forecasts of hydro‐meteorological time series.Rank‐Set Pair Analysis combined with wavelet de‐noising markedly improves forecasting accuracy.The performance of the proposed approach proves best among its present competitors even when the extreme value occurs.

Assessing the Interactions between Chlorophyll a and Environmental Variables Using Copula Method

AbstractEutrophication is a major water quality problem throughout the world. An understanding of the pattern of phytoplankton variation and the relationships between chlorophyll a and environmental variables can contribute to eutrophic lake management. In this study, the copula method is applied to discern the inherent relationship between chlorophyll a and environmental variables of Wulihu Lake to the north of Taihu Lake, China. The results show that the method can not only determine the correlation between chlorophyll a and environmental variables through Kendall’s τ but also determine the joint distribution without assuming the variables to be independent. Moreover, the return period of chlorophyll a can be obtained for given different environmental variables. This study may provide insights for policy makers wishing to improve the water quality of lakes and reservoirs.

A new method for wind speed forecasting based on copula theory

Abstract How to determine representative wind speed is crucial in wind resource assessment. Accurate wind resource assessments are important to wind farms development. Linear regressions are usually used to obtain the representative wind speed. However, terrain flexibility of wind farm and long distance between wind speed sites often lead to low correlation. In this study, copula method is used to determine the representative years wind speed in wind farm by interpreting the interaction of the local wind farm and the meteorological station. The result shows that the method proposed here can not only determine the relationship between the local anemometric tower and nearby meteorological station through Kendalls tau, but also determine the joint distribution without assuming the variables to be independent. Moreover, the representative wind data can be obtained by the conditional distribution much more reasonably. We hope this study could provide scientific reference for accurate wind resource assessments. HighlightsA copula method is developed to determine the representative wind speed in wind farm.The method proposed can determine the joint distribution without assuming the variables to be independent.The representative wind speed obtained by the conditional distribution is more reasonably.

A hybrid wavelet analysis–cloud model data‐extending approach for meteorologic and hydrologic time series

For scientific and sustainable management of water resources, hydrologic and meteorologic data series need to be often extended. This paper proposes a hybrid approach, named WA-CM (wavelet analysis–cloud model), for data series extension. Wavelet analysis has time-frequency localization features, known as “mathematics microscope,” that can decompose and reconstruct hydrologic and meteorologic series by wavelet transform. The cloud model is a mathematical representation of fuzziness and randomness and has strong robustness for uncertain data. The WA-CM approach first employs the wavelet transform to decompose the measured nonstationary series and then uses the cloud model to develop an extension model for each decomposition layer series. The final extension is obtained by summing the results of extension of each layer. Two kinds of meteorologic and hydrologic data sets with different characteristics and different influence of human activity from six (three pairs) representative stations are used to illustrate the WA-CM approach. The approach is also compared with four other methods, which are conventional correlation extension method, Kendall-Theil robust line method, artificial neural network method (back propagation, multilayer perceptron, and radial basis function), and single cloud model method. To evaluate the model performance completely and thoroughly, five measures are used, which are relative error, mean relative error, standard deviation of relative error, root mean square error, and Thiel inequality coefficient. Results show that the WA-CM approach is effective, feasible, and accurate and is found to be better than other four methods compared. The theory employed and the approach developed here can be applied to extension of data in other areas as well.