Dengfeng Liu

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.

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.

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.

Variations of Runoff and Sediment Load in the Middle and Lower Reaches of the Yangtze River, China (1950-2013).

On the basis of monthly runoff series obtained in 1950–2013 and annual sediment load measured in 1956–-2013 at five key hydrological stations in the middle and lower reaches of the Yangtze River basin, this study used the Mann-Kendall methods to identify trend and abrupt changes of runoff and sediment load in relation to human activities. The results were as follows: (1) The annual and flood season runoffs showed significant decreasing trends at Yichang station, and showed slight downward trends at Hankou and Datong stations, while the abrupt changes of dry season runoff at Yichang, Hankou and Datong stations occurred in about 2007 and the change points were followed by significant increasing trends. The construction of the Three Gorges Dam, which began to operate in 2003, influenced the variations of runoff in the mainstream of Yangtze River, but the effect weakened with the distance along the downstream direction from TGD. (2) Since the 1990s, annual sediment loads at Yichang, Hankou, and Datong stations have been decreasing significantly, and after 2002, the annual sediment load at Yichang dropped below that of Hankou and Datong. The dams and deforestation/forestation contributed to the significant decreasing trend of the sediment load. In addition, the Three Gorges Dam aggravated the downward trend and caused the erosion of the riverbed and riverbanks in the middle and lower reaches. (3) The runoff and sediment load flowing from Dongting Lake into the mainstream of the Yangtze River showed significant decreasing trends at Chenglingji station after 1970s, and in contrast, slight increase in the sediment flow from Poyang Lake to the mainstream of the Yangtze River at Hukou station were detected over the post-TGD period (2003–2013). The result of the study will be an important foundation for watershed sustainable development of the Yangtze River under the human activities.

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.