A Hybrid SNIP-ANN Model: A Novel Approach for Accurate River Flow Prediction

Abstract

Introduction

River flow prediction is crucial for water resource management, environmental protection, and flood control, yet the nonlinear nature of hydrological data poses challenges. Previous studies using models like Random Forest and LSTM faced limitations in accuracy and computational complexity. This research introduces the hybrid SNIP-ANN model, combining the novel SNIP algorithm with Artificial Neural Networks, to enhance prediction accuracy. The study focuses on time-series analysis of monthly flows in the Columbia and Niger Rivers, vital freshwater sources. The goal is to improve forecasting precision for better water management and reduced uncertainties.



Materials and Methods

The SNIP-ANN model is an advanced hybrid method for analyzing river discharge time series. In this model, the SNIP algorithm first preprocesses the raw streamflow data; it separates meaningful signal components from background noise by eliminating fluctuations and enhancing significant peaks. Then, an artificial neural network (ANN), inspired by the parallel processing nature of the human brain, learns the nonlinear relationships between past and present discharge values using the processed data. In this study, a three-month lag (Q(t-3)) was selected as the optimal input. By combining SNIP’s strength in feature extraction and ANN’s ability in pattern recognition, the SNIP-ANN model demonstrated high accuracy in prediction. The model was trained and validated using historical monthly discharge data from the Columbia and Niger rivers. Evaluation metrics such as RMSE and the correlation coefficient (R) indicated high precision and low error. The model’s simplicity and use of only discharge data make it suitable for real-time water resource management and applications in other domains like meteorology and economics. Additionally, its lightweight structure and short training time allow for implementation in systems lacking advanced computational facilities. The model’s generalizability also makes it a reliable choice for time series analysis under diverse climatic conditions. Applying this approach in other data-driven fields can improve prediction accuracy while reducing computational costs. Overall, the SNIP-ANN model offers an effective, lightweight, and intelligent solution for predicting streamflow behavior in complex hydrological systems.

Results and Discussion

In this study, in the first stage, the temporal modeling and in the second stage, the performance evaluation of monthly streamflow prediction for the Columbia and Niger rivers during the study period was carried out using the Artificial Neural Network (ANN) model and the hybrid SNIP-enhanced ANN (SNIP-ANN) model. To model the time-dependent behavior of streamflow, three time-lagged discharge values (one-, two-, and three-month delays) were used as input parameters. Among them, the three-month lag (Q(t–3)) was selected as the optimal input based on its stronger correlation with hydrological patterns such as seasonal variability, rainfall fluctuations, and groundwater storage. Seventy percent of the data were used for training and thirty percent for testing the models. Based on the evaluation results from R, RMSE, and other metrics such as the Taylor diagram, violin plot, and observed versus predicted plots, it was found that the SNIP-ANN model significantly outperformed the standard ANN in temporal modeling. For instance, in the Columbia River, the SNIP-ANN model achieved an R value of 0.7503 and an RMSE of 865.23, while the ANN model had an R of 0.3249 and a much higher RMSE of 1325.99. Similarly, for the Niger River, the SNIP-ANN reached an R of 0.9286 and an RMSE of 231.49, surpassing the ANN model, which scored an R of 0.8014 and an RMSE of 487.88. Moreover, the scatter plots revealed that the predicted values in the SNIP-ANN model were more concentrated along the ideal line (y = x), indicating higher prediction accuracy. The violin plots further supported this finding by showing that the SNIP-ANN predictions closely matched the actual distribution of flow values, while the ANN model showed greater deviations. In general, the SNIP-ANN model demonstrated superior capability in capturing nonlinear patterns, reducing prediction error, and improving generalization performance. These results highlight the model’s potential for real-time hydrological forecasting, especially under extreme flow conditions. Future research may focus on integrating ensemble learning strategies or additional preprocessing techniques to further enhance prediction accuracy across diverse hydrological scenarios.



Conclusion

In this study, the hybrid SNIP-ANN model was introduced as an innovative approach for river discharge prediction and compared with the traditional ANN model. The results showed that the hybrid model significantly improved prediction accuracy compared to the ANN model. By using a three-month time delay in the input data, the model was able to effectively simulate seasonal fluctuations and periodic changes in river discharge. The correlation coefficient for the hybrid model increased from 0.3249 to 0.7503 for the Columbia River and from 0.8014 to 0.9286 for the Niger River, while the prediction error (RMSE) decreased from 1325.99 to 865.23 and from 883.487 to 497.231, respectively. This improvement in performance was particularly noticeable in simulating extreme discharge fluctuations and filtering out additional noise in the data. The results indicate that the SNIP-ANN model is an effective tool for river discharge prediction and has high potential for applications in water resource management and flood prediction. This model can be highly beneficial in long-term forecasting and addressing challenges related to climate change and hydrological fluctuations.