# A Review of the Artificial Neural Network Models for Water Quality Prediction
## 4 Artificial Neural Networks Models for Water Quality Prediction
[117] utilized Monte Carlo simulation (MCS), a sensitivity analysis method that involves repeatedly generating a probability distribution of random input values, to ultimately create an ANN model with fewer inputs. Moreover, other input selection techniques include correlation analysis and genetic algorithm were tested. [PERSON] and [PERSON] [118] utilized sigmoid and linear transfer function in the hidden and output layer, respectively. Results showed that ANFIS and BPNN could predict DO with reasonable accuracy. [PERSON] et al. [119] adopted linear interpolation whose data increment was calculated by the slope of the assumed line to fill the missing data. Then, hierarchical ELM based on a hierarchical structure was chosen to model the DO, pH, and SS. The advantage of hierarchical ELM is able to learn sequential information online. Results demonstrated the effectiveness of the proposed methods. Researchers tended to divide the training set data into 70% to 90% of the total data [39; 42; 49; 52; 72; 120; 121; 122; 123; 124; 125; 126; 127]. [PERSON] et al. [35] divided the data into training (90%) and testing sets (10%). Then, they applied three typical MLP architectures to complete the Tur prediction whose inputs were NH\({}_{3}\)-N, EC, DO, pH, and WT. [PERSON] et al. [128] randomly divided the datasets and pointed out that when the data tended to be roughly periodic after a year, the time length of data acquisition, covering a long period such as a year or more was highly recommended in order to capture long-term variation. [PERSON] et al. [129] found the most significant parameters by using analysis of variance (ANOVA) techniques. Result indicated that rainfall records were the most significant parameters for turbidity forecasting. [PERSON] and [PERSON] [130] took the normalization step to control the scale of each feature, in the same range in case the difference of the order of magnitude will lead to the dominance of larger attributes thereby slowing down the iterative convergence. However, they did not give clear details about normalization. [PERSON] et al. [36] used time delay cells of TDNNs, designed based on the structure of MLPs, to deal with the dynamic nature of sample data. Then, they applied factor analysis to select the model inputs. Results illustrated that TDNN with 2 hidden layers of 15 neurons in each of the layers was the best architecture. [PERSON] et al. [9] provided a new solution to EC and TDS prediction. When the predictive variables were not available, researchers could realize the final predictions through modeling other relevant variables. They utilized monthly meteorological data RF, RO, and WL to forecast EC and TDS due to the lack of historical records of outputs. [PERSON] [82] introduced a Quasi-Newton method, Broyden-Fletcher-Goldfarb-Shanno (BFGS), to train the parameters of MLP in SS forecasting. [PERSON] et al. [60] conducted the sensitivity analysis of the obtained data and scaled it to between 0.1 to 0.9. Results indicated that RBFNN could achieve high-performance accuracy. [PERSON] and [PERSON] [47] utilized open-source data from Eight United States Geological Survey stations (USA) and preprocessed the data by standardization method. Several ELM models are applied for DO prediction. [PERSON] [131] discussed the Garson method to find the relative importance of each input variable. Results indicated that including meteorological and hydrologic variables could improve the accuracy of the modelswith fewer influential variables. [PERSON] et al. [32] and [PERSON] et al. [132] demonstrated the feasibility of the ANFIS method in predicting river water quality. This model overcame the shortcomings of ANN models such as overfitting and local minima, and combined fuzzy logic with ANN to provide a method to solve uncertain problems. [PERSON] et al. [133] took interrelated and easily measurable parameters of pH, EC, and Tur, as inputs to realize TN and TP predictions. [PERSON] et al.