Environmental Investment Prediction Using Extended Belief Rule-based System and Evidential Reasoning Rule

A scientific environmental investment prediction plays a crucial role in controlling environmental pollution and
avoiding the blind investment of environmental management. However, effective environmental investment prediction
usually has to fact three challenges about diversiform indicators, insufficient data, and the reliability of prediction models.
In the present study, a new prediction model is proposed using the extended belief rule-based system (EBRBS) and
evidential reasoning (ER) rule, called ensemble EBRBS model, with the aim to overcome the above challenges for better
environmental investment prediction. The proposed ensemble EBRBS model consists of two components: 1) multiple
EBRBSs, which are constructed on the basis of not only using various feature selection methods to select representative
indicators but also data increment transformation to enrich the training data; 2) an ER rule-based combination method,
which utilizes the ER rule to accommodate the weights and reliabilities of different EBRBSs with the predicted outputs of
these EBRBSs to have an integrated environmental investment prediction. A detailed case study is then provided for
validating the proposed model via extensive experimental and comparison analysis based on the real-world environmental
data about 25 environmental indicators for 31 provinces in China ranged from 2005 to 2018. The results demonstrate that
the ensemble EBRBS model can be used as an effective model to accurately predict environmental investments. More
importantly, the ensemble EBRBS model not only obtains a high accuracy better than some existing prediction models, but
also has an excellent robustness compared with others under the situations of excessive indicators and insufficient data.