The research into rainfall-runoff plays a very important role in water resource management. However, runoff simulation is a challenging task due to its complex formation mechanism, time-varying characteristics and nonlinear hydrological dynamic process. In this study, a nonlinear autoregressive model with exogenous input (NARX) is used to simulate the runoff in the Linyi watershed located in the northeastern part of the Huaihe river basin. In order to better evaluate the performance of NARX, a distributed hydrological model, TOPX, is used to simulate the discharge as a reference, and runoff classification by cluster analysis is used to further improve the accuracy of runoff simulation. Based on the four statistics indexes of the Nash–Sutcliffe efficiency (NSE), correlation coefficient (CC), root mean square error (RMSE) and mean relative bias (Bias), the NARX model is capable of simulating the rainfall-runoff dynamic process satisfactorily, although there is a little underestimation of the peak flow. After runoff classification, underestimation has been improved, and discharge simulation driven by NARX based on runoff classification (C-NARX) is well consistent with the observation. It is feasible to take it as a promising method, which also can be seen as a good reference and replacement for the current rainfall-runoff simulation.

With the aim of reducing the losses from water pollution, a dynamic risk assessment model for water quality is studied in this paper. This model is built on the projection pursuit cluster principle and risk indexes in the complex system, proceeding from the whole structure and its component parts. In this paper, the fuzzy analytic hierarchy process is used to screen out index system and determine index weight, while the further value of an index is simulated by hydrological model. The proposed model adopts the comprehensive dynamic evaluation method to analyze the time dimension data, and evaluates the development tendency by combining qualitative analysis with quantitative analysis. The projection pursuit theory is also employed for clustering the spatial dimension data, the optimal projection vector for calculating risk cluster type to compartmentalize risk, and then local conditions for proposing the regulation scheme. The applicational results show that the model has the strong logic superiority and regional adaptability with strict theoretical system, flexible methods, correct and reasonable results and simple implementation to provide a new way for research on risk assessment models of water quality.