## 4 Conclusions  
Predictive modelling of river flow based on meteorological weather data using the Multilayer Artificial Neural Networks (ANNs) Particle Swarm Optimization (PSO) algorithm has been discussed. Sungai Kelantan river flow data ranging from January 1988 to December 2016 was used. The results demonstrate the potential applications of ANNs as an artificial intelligence-machine learning tool  
Figure 4: The best testing performance for the cascade-forward neural networks trained with particle swarm optimization (CFNNPSO) model (\(R^{2}~{}=~{}0.88\)). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**ANN Structure**} & \multicolumn{4}{c}{**FFNNPSO**} & \multicolumn{4}{c}{**CFNNPSO**} \\ \cline{2-10} \# & \multicolumn{4}{c}{**Training**} & \multicolumn{4}{c}{**Testing**} & \multicolumn{4}{c}{**Training**} & \multicolumn{4}{c}{**Testing**} \\ \cline{2-10}  & \multicolumn{2}{c}{**Hidden Layer Neurons**} & \(R^{2}\) & **RMSE** & \(R^{2}\) & **RMSE** & \(R^{2}\) & **RMSE** & \(R^{2}\) & **RMSE** \\ \hline
1 & [5] & 0.74 & 193.0 & 0.87 & 199.7 * & 0.73 & 198.3 & 0.85 & 211.4 * \\ \hline
2 & [10] & 0.75 & 188.2 & 0.71 & 294.7 & 0.73 & 197.8 & 0.79 & 250.4 \\ \hline
3 & [15] & 0.74 & 193.1 & 0.76 & 266.7 & 0.72 & 199.2 & 0.81 & 238.5 \\ \hline
4 & [20] & 0.74 & 191.8 & 0.85 & 207.6 & 0.73 & 196.1 & 0.82 & 230.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Results on FFNN and CFNN trained using PSO with 3 features \((x_{1},x_{2},x_{4})\). to predict river flow variables based on meteorological and weather data as studied in this paper, where two ANNs structures were used: feed-forward neural networks (FFNN) and cascade-forward neural networks (CFNN). The PSO algorithm used to train the ANN has also contributed to the advancement of the predictive model building. Generally, ANNs with one hidden layer trained using PSO were able to produce acceptable accuracy and good generalization for both the training and testing dataset. This result is better than the prediction performance of the Multiple Linear Regression (MLR) trained via Lasso Regression/L1. Moreover, a parsimonious model with reduced features was proposed; this feature was carefully selected. From the parsimonious model experimentation, it was possible to build an ANNs predictive model that can achieve acceptable accuracy in predicting river flow by using only weighted rainfall, average evaporation and max temperature as input variables. The experimentation results also indicate that CFNN trained using the PSO algorithm has more robust generalization performance compared to FFNN in the reduced feature (parsimonious) model. The model accuracy can still be improved using advanced techniques in machine learning modelling such as the ensemble method, improvement of the optimizer and cross-validation training procedure. Furthermore, future research will work on some areas including benchmarking with other machine learning algorithms, benchmarking with other mete-heuristic algorithms for ANN training, data augmentation to enhance the diversity of the available data without generating actual data and real-time deployment of the predictive model in the Internet of Things (IoT) scenario. Despite the efficiency of ANNs as a black box model for river flow modelling, further exploration of research in this area is required. These include an automated feature selection mechanism, the possibility of using Deep learning neural networks regression, and improvement of accuracy to reduce overfitting via different optimizer algorithms. Another area includes the deployment stage of the machine learning model, which can involve Big Data, IoT and the Cloud computing platform. As AI tools in this regard are easily available nowadays, the area of this study promises high applicability of hydro-informatics systems, especially in Malaysia. This hydro-informatics concept and implementation need more extensive attention by authorities and decision makers to deal with water resource management which is currently a serious issue in some countries.