The gap in existing studies is the lack of investigation

The gap

Evidence profile

Sourced from the stated research gap and future work of the source papers, classified as general, spanning 3 journals.

Supporting evidence — 3 representative gaps

• Overcoming Generalization Issues in Flood Prediction: A Machine Learning Approach Across Multiple Basins (2026) · Sustainability · doi

  The gap in existing studies is the lack of investigation into the challenges of generalization in flood prediction models using machine learning techniques. The gap in existing models is their limited performance in predicting smaller events. The gap in existing studies is the oversimplification of flood causes to meteorological parameters alone.

  generalstated research gap
  Keywords: gap existing studies lack investigation challenges generalization flood
• A new machine learning technique for predicting river water quality using AVOA-RNN (2026) · Water Science · doi

  This study proposed a hybrid AVOA-RNN framework for predicting river water quality using the Cauvery River data- set. By integrating advanced imbalance-handling strategies (SMOTE, SMOGN) with African Vulture Optimization for hyper-parameter tuning, the model demonstrated supe- rior predictive capability in both regression and classifica- tion tasks. In classification, the proposed model achieved 97.3% accuracy, 99.52% sensitivity, 97.42% specificity, and a ROC-AUC of 0.97, outperforming comparative approaches such as CNN (93.85%), LSTM (94.92%), RNN (95.73%), GRU (96.38%), WOA-NN (0.82), GA-LSTM (0.80), EMD- (F1 = 0.86), WOA-LSTM (F1 = 0.84), AQPSO-SOFNN Y. et al. Water Science (2026) 40:16 Page 19 of 21 Fig. 11 SHAP-based feature importance (dissolved oxygen (DO ≈ 1.3), BOD (≈ 0.65), pH (≈ 0.45), and TDS (≈ 0.25) dominate, while NO₃, Cl, PO₄, SO₄, turbidity, and FC contribute marginally) Fig. 12 Comparative SHAP analysis for the existing models Y. et al. Water Science (2026) 40:16 Page 20 of 21 and SOFNN-HPS (Accuracy = 0.87). In regression tasks, AVOA-RNN obtained the lowest RMSE (8.3) compared to WOA-NN (9.0), GA-LSTM (9.6), EMD-WOA-LSTM (8.8), AQPSO-SOFNN (8.7), and SOFNN-HPS (8.6), along with a high coefficient of determination ( R2 =0.91). These results confirm that AVOA-RNN not only surpasses tradi- tional and hybrid baselines but also ensures greater ecolog- ical interpretability through SHAP-based feature analysis, highlighting DO, BOD, pH, and TDS as dominant param- eters. The framework thus provides a robust and interpret- able solution for sustainable water quality monitoring and management.

  generalfuture workevidence 5/5
  Keywords: lstm water sofnn avoa shap proposed hybrid framework river quality model regression tasks accuracy comparative
• Evaluation of flood susceptibility through an artificial neural network-based differential evolution optimization algorithms and GIS techniques (2026) · Natural Hazards · doi

  In this study, the flood susceptibility of the Putna River basin was assessed using advanced machine learning models, namely MLP and three optimized hybrid approaches: ABC–MLP, EHO–MLP and DE–MLP, integrated in a GIS environment. The combination of these mod- Natural Hazards (2026) 122:4061 3 406 Page 28 of 32 els for the first time in the literature for flood susceptibility estimation and their integration in the GIS environment represents the main novelty of the present research work. The comparative analysis clearly demonstrated the superiority of the hybrid models over the classic MLP neural network, highlighting the essential role of optimization algorithms in increasing predictive performance. Among the tested models, DE–MLP achieved the best results, recording an accuracy of 0.982 and an AUC-ROC value of 0.985 on the test set, indicating an excellent discrimination capacity between flood-prone and unaffected areas. Also, the high values of precision, recall and F1 score (all 0.982) confirm the balanced and robust nature of this model. The EHO–MLP and ABC–MLP models performed very well, with accuracies of 0.964 and 0.946, respectively, and AUC-ROC values of 0.970 and 0.975, but lower than the DE–MLP model. The simple MLP model performed the worst (AUC-ROC = 0.945), highlighting the clear advantage of hybrid approaches. The implications of this work also explain the better flood management with the optimized models produce flood susceptibility maps that can be used for better flood management. High risk areas can be targeted for flood mitigation, reducing impact on communities and infrastructure. And finally, accurate flood susceptibility assessment means better resource allocation, focusing on high risk areas. The analysis of the importance of variables indicated slope (0.334), altitude (0.249), distance from the river (0.244) and rainfall (0.183) as the dominant factors in controlling the flooding processes. The final susceptibility maps highlight the high-risk areas, providing valuable support for territorial planning, flood risk management and decision-making at local and regional levels. The results confirm that the DE–MLP model represents an efficient and reliable solution for assessing flood susceptibility in regions with similar characteristics. Based on the new findings of this work, as well as its limitations, the future works can consider other areas to explore with different geography and climate to test the models. In addition, real time data such as rainfall and river flow for dynamic flood susceptibility assessment is an interesting problem and they may assist improve the flood susceptibility mapping works. Combine multiple machine learning algorithms to create more accurate models is also a great work that can build on this research and improve flood risk management.

  generalfuture workevidence 5/5
  Keywords: flood models susceptibility areas risk high model writing river hybrid better management project grant conceptualization

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