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.

Climate change information is essential for water resources management planning, and the majority of research available uses the global circulation model (GCM) data to project future water balance. Despite the fact that the results of various GCMs are still heterogeneous, it is common to utilize GCM values directly in climate change impact assessment models. To mitigate these limitations, this study provides an alternative methodology, which uses GCM-based data to assign weights on historical scenarios rather than to directly input their values into the assessment models, thereby reducing the uncertainty involved in the direct use of GCMs. Therefore, the real innovation of this study is placed on the use of a new probability weighting scheme with multiple GCMs rather than on the direct input of GCM-driven data. Applied to make future projections of the water shortage in the Han River basin of Korea, the proposed methodology produced conservative but realistic projection results (15% increase) compared to the existing methodologies, which projected a dramatic increase (144%) in water shortage over 10 years. As a result, it was anticipated that the amount of water shortages in the Han River basin would gradually increase in the next 90 years, including a 57% increase in the 2080s.

Recently, flood damages have increased worldwide due to global warming and climate change. According to the Report of the Intergovernmental Panel on Climate Change (IPCC), extreme weather events such as heat wave, drought, and flood are expected to increase in the future. It is thus necessary to realize that larger and more frequent flood disasters are rapidly nearing us, and need to prepare for them accordingly. This research aims to provide a framework for extreme flood management, beginning with the establishment of extreme flood definition from a more general point of view through reviewing existing literature and references. Also, the purpose of this research is to identify the current status of flood forecasting in the ESCAP/WMP Typhoon Committee’s member countries and to provide recommendations on extreme flood forecasting and flood risk management.in member countries.