A wide range of quantitative and qualitative modelling research on ecosystem services
(ESS) has recently been conducted. The available models range between elementary …

Hydrodynamic models are commonly used to understand flood risk and inform flood
management decisions. However, their high computational cost can impose practical limits …

Abstract Bayesian Belief Networks (BBNs) are being increasingly used to develop a range of
predictive models and risk assessments for ecological systems. Ecological BBNs can be …

The accidental leakage of industrial wastewater containing heavy metals from enterprises
poses great risks to resident health, social instability, and ecological safety. During 2005 …

Mine tailings ponds that contain heavy metals are sources of potential risk to human security
and ecosystem health. China particularly faces challenge of accidental water pollution risk …

Validation is a critical component of any modelling process. In artificial neural network (ANN)
modelling, validation generally consists of the assessment of model predictive performance …

Data-driven models can achieve high accuracy and low computational cost without a priori
knowledge of hydrological system, which have been successfully applied in streamflow …

Input variable selection (IVS) is one of the most important steps in the development of
artificial neural network and other data driven environmental and water resources models …

Artificial neural network (ANN), a data driven modeling approach, is proposed to predict the
water quality indicators of Lake Fuxian, the deepest lake of southwest China. To determine …

Wastewater treatment is a complicated dynamic process, the effectiveness of which is
affected by microbial, chemical, and physical factors. At present, predicting the effluent …