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Discharge from the largest karst spring in north China, the Niangziguan Springs, has been declining since the 1950s. This paper examines the response of these springs to climatic change and anthropogenic influence by attempting a model-based discrimination between phases in the stream discharge record. In Niangziguan Springs Basin, the exploitation of karst groundwater began in 1979. Accordingly, the spring discharge data were divided into two phases: pre-1979 and post-1979. In the first phase (1957-78) the spring discharge was believed to be affected solely by climate change, and in the second phase (1979-2007) the spring discharge was influenced by both climate change and human activities. Using grey system theory, a discharge model was estimated for the first phase. Extrapolating the model, we obtained a projection of the spring discharge during the second phase. Using a water balance calculation, we discerned the respective effects of climate change and human activities on depletion of spring discharge for the second phase. The results show that the contribution of climate change to depletion of Niangziguan Springs is 2.30m3/s and the contribution of anthropogenic activities ranges from 1.89 to 2.90 m3/s, although this assumes a constant for the climate change effect. Accordingly, the anthropogenic effects have been approaching and surpassing the effects of climate change during the second phase. With respect to the impact of human activities on spring discharge, groundwater abstraction accounts for only about 34-52% of the declines; 48-66% of the declines are related to other human activities, such as dewatering from coal mining, dam building and deforestation. [ABSTRACT FROM AUTHOR]

Product-sum-type fuzzy controllers are known to have similar characteristics to PD-type controllers. In the case of type-0 control systems, PID-type fuzzy controllers have been proposed in the literature in order to eliminate the steady-state error. However, these control methods, essentially based on conventional PID theory, have no predictive capabilities. The concept of grey system theory, which has a certain prediction capability, offers an alternative approach for various kinds of conventional control methods, such as PID control and fuzzy control. This paper proposes a grey prediction-based fuzzy PID controller that can overcome the stated shortcomings. In order to obtain a better controller performance, another fuzzy controller is designed to change the step size of the grey predictor. A non-linear liquid level system is taken as a test bed. The grey model developed is examined under several different conditions and it is shown that the proposed grey fuzzy PID controller can predict the future output value of the system. It is clear that the proposed adaptive PID-type fuzzy controller is effective in controlling such a non-linear system accurately by changing the step size adaptively for real-time working. [ABSTRACT FROM AUTHOR]