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We discuss the accuracy and performance of the adaptive neuro-fuzzy inference system (ANFIS) in training and prediction of dissolved oxygen (DO) concentrations. The model was used to analyze historical data generated through continuous monitoring of water quality parameters at several stations on the Johor River to predict DO concentrations. Four water quality parameters were selected for ANFIS modeling, including temperature, pH, nitrate (NO3) concentration, and ammoniacal nitrogen concentration (NH3-NL). Sensitivity analysis was performed to evaluate the effects of the input parameters. The inputs with the greatest effect were those related to oxygen content (NO3) or oxygen demand (NH3-NL). Temperature was the parameter with the least effect, whereas pH provided the lowest contribution to the proposed model. To evaluate the performance of the model, three statistical indices were used: the coefficient of determination (R (2)), the mean absolute prediction error, and the correlation coefficient. The performance of the ANFIS model was compared with an artificial neural network model. The ANFIS model was capable of providing greater accuracy, particularly in the case of extreme events.

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Water quality analysis is a crucial step in water resources management and needs to be addressed urgently to control any pollution that may adversely affect the ecosystem and to ensure the environmental standards are being met. Thus, this work is an attempt to develop an efficient model using support vector machine (SVM) to predict the water quality of Langat River Basin through the analysis of the data of six parameters of dual reservoirs that are located in the catchment. The proposed model could be considered as an effective tool for identifying the water quality status for the river catchment area. In addition, the major advantage of the proposed model is that it could be useful for ungauged catchments or those lacking enough numbers of monitoring stations for water quality parameters. These parameters, namely pH, Suspended Solids (SS), Dissolved Oxygen (DO), Ammonia Nitrogen (AN), Chemical Oxygen Demand (COD), and Biochemical Oxygen Demand (BOD) were provided by the Malaysian Department of Environment (DOE). The differences between dual scenarios 1 and 2 depend on the information from prior stations to forecast DO levels for succeeding sites (Scenario 2). This scheme has the capacity to simulate water-quality accurately, with small prediction errors. The resulting correlation coefficient has maximum values of 0.998 and 0.979 after the application of Scenario 1. The approach with Type 1 SVM regression along with 10-fold cross-validation methods worked to generate precise results. The MSE value was found to be between 0.004 and 0.681, with Scenario 1 showing a better outcome.

In nature, streamflow pattern is characterized with high non-linearity and non-stationarity. Developing an accurate forecasting model for a streamflow is highly essential for several applications in the field of water resources engineering. One of the main contributors for the modeling reliability is the optimization of the input variables to achieve an accurate forecasting model. the main step of modeling is the selection of the proper input combinations. Hence, developing an algorithm that can determine the optimal input combinations is crucial. this study introduces the Genetic algorithm (GA) for better input combination selection. Radial basis function neural network (RBFNN) is used for monthly streamflow time series forecasting due to its simplicity and effectiveness of integration with the selection algorithm. In this paper, the RBFNN was integrated with the Genetic algorithm (GA) for streamflow forecasting. The RBFNN-GA was applied to forecast streamflow at the High Aswan Dam on the Nile River. The results showed that the proposed model provided high accuracy. The GA algorithm can successfully determine effective input parameters in streamflow time series forecasting. BackgroundThe inflow parameter is a significant component of the hydrological process in water resources. Accurate forecasting of river flows for long-term and short-term forecasts are crucial to solving different water engineering problems (e.g., designing agricultural land and flood protection works for urban areas) 1 . Accurate and reliable flow forecasting is a vital reference for making decisions in reservoir system control. Hence, streamflow forecasting modeling has attracted attention and great advances in this field have been developed in recent decades 2 .Conventional models (linear models) cannot capture the non-linearity and non-stationary of hydrological applications. The autoregressive moving average (ARMA) model, autoregressive model, and autoregressive integrated moving average (ARIMA) model are linear models that have been applied in hydrological time series forecasting 3-5 . The need for determining models capable of addressing the nonlinearity and non-stationary that are characteristics of natural reservoir inflow data has led researchers to propose advanced methods 6,7 . Recently, artificial intelligence methods showed relatively good forecasting accuracy. However, they had trouble detecting the highly stochastic pattern of the data.The most popular example of artificial intelligence methods is the artificial neural network (ANN). Wu et al. 8 established the Feed Forward Neural Network (FFNN) model for streamflow simulation. The finding evidenced the potential of the FFNN model for streamflow modeling. Two algorithms including multilayer perceptron (MLP) and radial basis function neural network (RBFNN) developed for river flow prediction 9 . The authors reported that the MLP model outperformed the RBNN model. Danandeh Mehr et al. 10 investigated the ability of successive station forecasting models using ANN in a rain gauge-...