bayesian network model

It is important to predict debris flood for reducing its damages. The aim of this study is the prediction of sediment concentration of debris floods and ordinary floods using bayesian network (BN) and artificial neural network (ANN) models in Ammameh, Navrood and Casilian basins which were located in Tehran, Gilan and Mazandaran provinces, respectively. Accordingly, average basin elevation (EL), average basin slope (S), watershed area (A), current day rainfall (R), antecedent rainfall (AR) of three-days ago and discharge of one-day ago were selected as input variables. Then, 32 scenarios were tested to determine the most effective factors on the sediment concentration of flood. For the scenario derived from all selected factors, indices R2 and MAPE in the test stage were obtained 0.97 and 8.55%, respectively. Assessment of the effect of different factors shows that the most effective factors on the BN model’s prediction accuracy are EL, R, PQ, A and AR one-day ago. Indices R2 and MAPE for this scenario were obtained 0.916 and 11.01%, respectively. It was selected as the best scenario because the least number of predictors and the highest accuracy. The most effective factors identified in this study can be used to predict debris flood in similar basins.

Nowadays, the using of treated wastewater in diverse purposes such as agriculture, industry and irrigation is converted to an appropriate solution for water shortage. Despite the advantages of water reuse, due to various pollutants in wastewater and lack of complete treatment in accordance with the standards, it is always risky. Therefore risk assessment should be conducted in order to determine the reliability of system and offer solutions to enhance the reliability of wastewater treatment system. Risk is the possibility of occurrence an adverse event and the severity of the negative effects of it. In the present study, Bayesian network approach is used for risk assessment of wastewater treatment plant. In this method the possibility of an event can be obtained by creating cause and effect relationship between the components of the system.
Wastewater treatment plant of industrial complex of Moorchekhort is selected for case study in this study. The fouling, corrosion and biofilm of using treated wastewater for industry have been determine as final events. The model input datas have been formed by experts, specialists and laboratory datas from wastewater treatment plant of industrial complex of Moorchekhort. The reliability of wastewater treatment system analyzed by Bayesian network model showed %70. The efficiency of Bayesian network in order to determine elements of failure and estimate the risk of wastewater treatment system that is not adapted to effluent standards for the industry is shown in this study.