Conceptual rainfall–runoff models are widely used to understand the hydrologic responses of catchments of interest. Modellers calculate the model performance statistics for the calibration and validation periods to investigate whether these models serve as satisfactory representations of the natural hydrologic phenomenon. Another useful method to investigate model suitability is sensitivity analysis (SA), which investigates structural uncertainty in the models. However, a comprehensive method is needed, which led us to develop a model suitability index (MSI) by combining the results of model performance statistics and SA. Here, we assessed and compared the suitability of three rainfall–runoff models (GR4J, IHACRES and Sacramento model) for seven Korean catchments using MSI. MSI showed that the GR4J and IHACRES models are suitable, having more than 0.5 MSI, whereas the Sacramento has less than 0.5 MSI, representing unsuitability for most of the Korean catchments. The MSI developed in this study is a quantitative measure that can be used for the comparison of rainfall–runoff models for different catchments. It uses the results of existing model performance statistics and sensitivity indices; hence, users can easily apply this index to their models and catchments to investigate suitability.

Rainfall–runoff models are not perfect, and the suitability of a model structure depends on catchment characteristics and data. It is important to investigate the pros and cons of a rainfall–runoff model to improve both its high- and low-flow simulation. The production and routing components of the GR4J and IHACRES models were combined to create two new models. Specifically, the GR_IH model is the combination of the production store of the GR4J model and the routing store of the IHACRES model (vice versa in the IH_GR model). The performances of the new models were compared to those of the GR4J and IHACRES models to determine components improving the performance of the two original models. The suitability of the parameters was investigated with sensitivity analysis using 40 years’ worth of spatiotemporally different data for five catchments in Australia. These five catchments consist of two wet catchments, one intermediate catchment, and two dry catchments. As a result, the effective rainfall production and routing components of the IHACRES model were most suitable for high-flow simulation of wet catchments, and the routing component improved the low-flow simulation of intermediate and one dry catchments. Both effective rainfall production and routing components of the GR4J model were suitable for low-flow simulation of one dry catchment. The routing component of the GR4J model improved the low- and high-flow simulation of wet and dry catchments, respectively, and the effective rainfall production component improved both the high- and low-flow simulations of the intermediate catchment relative to the IHACRES model. This study provides useful information for the improvement of the two models.

The process of implementing neural networks in a computer system is known as deep learning. In this study, a deep learning model, namely long short-term memory (LSTM), was established to predict and forecast water levels for stations located at the Nam Ngum River Basin in Lao PDR. Water levels are predicted and forecasted based on the rainfall and water level data observed at previous time steps. It is proposed that the optimal sequence length for modeling should be determined based on the threshold of the correlation coefficient obtained from the water level and rainfall time series. The trained LSTM models in this study can be considered fair and adequate for water level prediction, as NSE values from 0.5 to 0.7 were mostly obtained from the model validations in the testing periods. The results showed that the autocorrelation and cross-correlation analysis did help in determining the optimal sequence length in an LSTM model. The performance levels of the LSTM model in forecasting future water levels in the Nam Ngum River Basin varied; the forecasted water level hydrographs for the Pakkayoung station generally corresponded with the observed ones, while the forecasted water level hydrographs for the other stations deviated significantly from the observed hydrographs.

This study explores the application of a Long Short-Term Memory (LSTM) model for river water level forecasting, emphasizing the critical role of hyper-parameters optimization. Similar to physical and numerical rainfall-runoff models, LSTM relies on parameters to drive its data-driven modeling process. The performance of such models is highly sensitive to the chosen hyper-parameters, making their optimization essential. To address this, three algorithms—Grid Search, Random Search, and Bayesian Search—were applied to identify the most effective hyper-parameter combinations. Cross-correlation analysis revealed that average rainfall had a stronger influence on river water levels than upstream point rainfall, leading to its selection as the model input. The optimization focused on five key hyper-parameters: neuron units, learning rate, dropout rate, number of epochs, and batch size. Results showed that, while Grid Search required the most computational time, both Random and Bayesian Search were more efficient. Notably, Bayesian Search yielded the best predictive performance with minimal time cost, making it the preferred optimization method. Additionally, reproducible LSTM simulations were conducted to ensure the consistency and practical applicability of the forecasting in real-world scenarios. Overall, Bayesian Search is recommended for optimizing LSTM models due to its balance of accuracy and computational efficiency in hydrological forecasting.

Ensuring the quality of hydrological data is critical for effective flood forecasting, water resource management, and disaster risk reduction, especially in regions vulnerable to typhoons and extreme weather. This study presents a framework for quality control and outlier detection in rainfall and water level time series data using both supervised and unsupervised machine learning algorithms. The proposed approach is capable of detecting outliers arising from sensor malfunctions, missing values, and extreme measurements that may otherwise compromise the reliability of hydrological datasets. Supervised learning using XGBoost was trained on labeled historical data to detect known outlier patterns, while the unsupervised Isolation Forest algorithm was employed to identify unknown or rare outliers without the need for prior labels. This established framework was evaluated using hydrological datasets collected from Lao PDR, one of the member countries of the Typhoon Committee. The results demonstrate that the adopted machine learning algorithms effectively detected real-world outliers, thereby enhancing real-time monitoring and supporting data-driven decision-making. The Isolation Forest model yielded 1.21 and 12 times more false positives and false negatives, respectively, than the XGBoost model, demonstrating that XGBoost achieved superior outlier detection performance when labeled data were available. The proposed framework is designed to assist member countries in shifting from manual, human-dependent processes to AI-enabled, data-driven hydrological data management.

Various uncertainty analysis methods have been used in various studies to analyze the uncertainty of rainfall-runoff models; however, these methods are difficult to apply immediately as they require a long learning time. In this study, we propose a simple uncertainty-screening method that allows modelers to investigate relatively easily the uncertainty of rainfall-runoff models. The 100 best parameter values of three rainfall-runoff models were extracted using the efficient sampler DiffeRential Evolution Adaptive Metropolis (DREAM) algorithm, and the distribution of the parameter values was investigated. Additionally, the ranges of the values of a model performance evaluation statistic and indicators of hydrologic alteration corresponding to the 100 parameter values for the calibration and validation periods was analyzed. The results showed that the Sacramento model, which has the largest number of parameters, had uncertainties in parameters, and the uncertainty of one parameter influenced all other parameters. Furthermore, the uncertainty in the prediction results of the Sacramento model was larger than those of other models. The IHACRES model had uncertainty in one parameter related to the slow flow simulation. On the other hand, the GR4J model had the lowest uncertainty compared to the other two models. The uncertainty-screening method presented in this study can be easily used when the modelers select rainfall-runoff models with lower uncertainty.