These three parameters are only the climate parameters – mean daily precipitation, maximum temperature, and minimum temperature. Then, the Radial Basis Function neural network (RBFNN) predictions of WQPs EC, DO, BOD, and nitrate using only these three input parameters are performed and yielded correlation coefficients less than 0.50 using MATLAB. If we see Table 9, the cumulative variance up to 98% is explained better using the first five input parameters, namely mean daily precipitation, maximum and minimum temperatures, agricultural land use factor, and forest land use factor. Therefore, RBFNN simulations are performed for EC, DO, BOD, and nitrate using these five input parameters to obtain correlation coefficients as shown in Table 10 for the entire dataset. We can see that the four parameters’ overall (R) values got slightly better than the results of simple feedforward neural networks obtained in Table 8 (with all the inputs). These results are better than the preliminary ANN results of Anmala et al. (2015) and as good as Venkateshwarlu et al. (2020) which are explored for the Upper Green River water quality data. In the current study and in Anmala et al. (2015), separate ANNs are explored for each of the WQP, whereas composite neural networks are explored for simultaneous, multiple output parameter predictions in Venkateshwarlu et al. (2020). The real-time and fast predictions are made for WQPs of the Upper Green River Watershed using extreme learning machine (ELM) networks in Anmala & Turuganti (2021). While the PCA results of Venkateshwarlu et al. (2020) indicated the effectiveness of only climate parameters (precipitation and temperature), the current PCA results indicate the effectiveness of climate and two land use factors – agricultural and forest in accurate water quality prediction. Venkateshwarlu et al.'s (2020) study was performed on the Karst watershed, i.e., Upper Green River Basin, Kentucky, USA while the current study dealt with the non-Karst watershed of the Godavari River Basin. All of these studies including the current study have developed ANNs for WQP predictions in a causal modeling framework. The results essentially outline the importance, potential, and applicability of ANNs for highly nonlinear stream water quality problems in Karst and non-Karst river basins. There have been many other studies of ANNs, where one WQP is predicted from remaining or available other WQPs or using its own time history, or using simply correlations between them and not so much using a causal modeling framework of climate and land use parameters as in the current study. Di Nunno et al. (2022) predict the nitrate concentrations in the Susquehanna River and the Raccoon River, USA accurately (R² = 0.77 and 0.94) using recurrent neural networks and time-series models with exogenous inputs such as water discharge, water temperature, dissolved oxygen, and specific conductance. Rajwade et al. (2021) predict BOD from 15 different combinations of available physical, chemical, and biological water quality parameters for Gola River, Uttarakhand, India, and obtained a maximum R² value of 0.997 using ANNs compared with a maximum of 0.861 using multiple linear regression. Ravansalar & Rajaee (2015) obtained an R² value of 0.949 using wavelet-based ANNs against a value of 0.381 using ANNs in the prediction of electrical conductivity for Asi River, Turkey. Alam et al. (2021) have used Weighted Regression on Time, Discharge and Seasons (WRTDS) to analyze the long-term trends of water quality especially that of BOD, DO, and nitrate-nitrite (NN), and found the influences of wastewater treatment plants (WWTPs), combined sewage outflows (CSOs), and agricultural runoff in increase of pollution levels for White River at Muncie, IN, USA. However, the current study is limited to the temporal prediction of WQPs from climate and land use parameters and has not considered the influences of discharge and seasons separately. The influence of discharge is intrinsically considered in the climate parameter, i.e., precipitation as one of the model inputs in the current study. The importance of N pressure from agriculture on surface water quality (D'Haene et al. 2022) can be seen in the current model's selective inputs decided by PCA, and the current model can be further used for nitrogen mitigation measures to achieve a good surface water quality status. The above-stated effective five inputs are used again in feedforward neural networks for better predictions and obtainment of functional form for water quality variables such as electrical conductivity, which is discussed in the next section.