Flood Water Level Prediction Research Articles

Predicting Flood Water Level Using Combined Hybrid Model of Rainfall-Runoff and AI-Based Models

Vietnam faces on significant human and property losses from floods almost every year. Therefore, the aim of this study is to provide timely and highly accurate flood prediction information using a developed hybrid model by combination of the rainfall-runoff model and AI-based model. We used Tank model as a rainfall-runoff model for peak flood discharge and parameter calibration was conducted by GA (genetic algorithm) and PS (pattern search). The sum of squared residuals (SSR) and weighted sum of squared residuals (WSSR) as objective functions were used for peak flood discharge evaluation. The simulated flood discharge was converted as flood water level by rating curve and Monte Carlo simulation was applied to estimate the confidence bounds for 95% confidence level. These estimates were then utilized as input data for the AI-based models to identify the strengths and weaknesses of each model and develop an optimal flood water level prediction model. Two AI-based models, deep neural network (DNN) and long short-term memory (LSTM), were used for flood water level prediction. The LSTM model demonstrated the best performance with a correlation coefficient (CC) of 0.98, normalized root mean square error (NRMSE) of 0.04, and Nash-Sutcliffe efficiency (NSE) of 0.98.

Application of AI-Based Models for Flood Water Level Forecasting and Flood Risk Classification

Owing to global climate change, the frequency of disasters has increased twelve-fold, with a corresponding approximately seventeen-fold increase in economic damages over the past six decades. Notably, severe flood damage has been occurring in Asia along the Pacific coast due to extreme weather events, including torrential rains and typhoons, which have been becoming increasingly frequent and prolonging the rainy season. In the Eastern Visayas region, the management and monitoring facilities for flood observation data, as well as the forecasting and warning systems suitable for the local area, are insufficient. The warning system introduced through overseas grants is limited in operation in some areas of the city. Furthermore, although an organization has jurisdiction over flood forecasting and warning, the system's operation is not systematic and is limited. Additionally, there is a shortage of technical manpower. In this study, we utilized deep learning models to forecast flood water levels in the CarayCaray Basin on Biliran Island, located in Eastern Visayas, the Philippines. Additionally, a flood risk classification was applied to evaluate the degree of risk associated with the predicted water levels. The predicted water levels for each model were compared with the observed water level data. The evaluation of the predictive performance of each model resulted in an NRMSE value of 9.48. Moreover, the accuracy of the DNN model was found to be the best among the flood water level prediction models. To implement the flood risk classification, we utilized extreme gradient boosting, random forest, and decision tree models. The application of these models resulted in an F1-score of 0.92 for the extreme gradient boost model, which exhibited the highest accuracy. With an increasing need for disaster (flood) management, AI-based predictive models are anticipated to reduce the damage caused by natural disasters and enhance disaster mitigation systems. Real-time collection of rainfall and water level data enables continuous learning. Furthermore, if a clear flood warning based on learned flood level patterns is issued, preemptive measures can be taken before intense flood damage occurs.

Water level prediction of Lake Poyang based on long short-term memory neural network

湖泊水位是维持其生态系统结构、功能和完整性的基础.鄱阳湖受流域五河和长江来水双重影响，水位变化复杂.为了准确预测鄱阳湖水位变化，采用长短时记忆神经网络方法（LSTM）构建了鄱阳湖水位预测模型.该模型以赣江、抚河、信江、饶河和修水五河入湖流量和长江干流流量作为输入条件，预测鄱阳湖湖区不同代表站（湖口、星子、都昌、吴城和康山）的水位过程.研究以1956-1980年的水文时间序列数据作为训练集，1981-2000年作为验证集，探讨了LSTM模型输入时间窗、隐藏神经元数目、初始学习率等模型参数对预测精度的影响，并确定了鄱阳湖水位预测模型的最优参数.结果表明，采用LSTM神经网络方法可基于流域五河和长江来水量历时数据合理预测鄱阳湖不同湖区的水位过程，五站水位预测的均方根误差为0.41~0.50 m，纳什效率系数和决定系数达0.96~0.98.为考察模型训练数据集对鄱阳湖水位预测结果的影响，进一步选取了随机5年（1956-1960年）的资料和5个典型水文年（1954年、1973年、1974年、1977年和1978年）的日均流量资料来训练模型.结果显示随机5年资料作为训练数据的预测精度要差于典型年水文资料训练得到的模型，尤其是洪、枯水位的预测；由于典型水文年数据量仍远低于20年的资料，故其总体预测精度要略低于采用20年资料训练的模型.建议应用这类基于数据驱动的模型时，应该尽可能多选取具有代表性的资料来训练.;Lake water level is the basis for maintaining the structure, function, and integrity of its ecosystem. The water level change of Lake Poyang is complicated as it was affected by five rivers within the basin and the Yangtze River. To accurately predict the water level change of Lake Poyang, the long short-term memory (LSTM) is used to construct the water level prediction model of Lake Poyang. The model uses the flows of the Ganjiang River, Fuhe River, Xinjiang River, Raohe River, Xiushui River and the mainstream of the Yangtze River as input conditions to predict the water level process of different representative stations in the Lake Poyang area (Hukou, Xingzi, Duchang, Wucheng and Kangshan). The hydrological time series data from 1956 to 1980 is used as the training set, and data from 1981 to 2000 was used as the verification set. The influence of model parameters such as input time window, hidden neuron nodes and initial learning rate on prediction accuracy is discussed. The optimal parameters of the Lake Poyang water level prediction model are determined. The results show that the LSTM can accurately predict the water level at different parts of Lake Poyang based on the water flow from the five rivers and the Yangtze River. The RMSE value of the five stations is 0.41-0.50 m, and the NSE and R² are 0.96-0.98. In order to investigate the impact of the model training set on the water level prediction results of Lake Poyang, the study further selects data from 5 random years (1956-1960) and 5 typical hydrological years (1954, 1973, 1974, 1977 and 1978) daily average flow data to train the model. The results show that the prediction accuracy of random 5 years data as training set is worse than that of typical annual hydrological data training, especially the prediction of flood and dry water level; since the typical hydrological data volume is still much lower than 20 years of data, the overall prediction accuracy is slightly lower than the model with 20 years of data training. Therefore, representative data should be selected as much as possible for training, when applying such a data-driven LSTM neural network model.

Flood Water Level Mapping and Prediction Due to Dam Failures

Sembrong dam has undergone overflow failure. Flooding has been reported to hit the town, covering an area of up to Parit Raja, located in the district of Batu Pahat. This study aims to identify the areas that will be affected by flood in the event of a dam failure in Sembrong Dam, Kluang, Johor at a maximum level. To grasp the extent, the flood inundation maps have been generated by using the InfoWorks ICM and GIS software. By using these maps, information such as the depth and extent of floods can be identified the main ares flooded. The flood map was created starting with the collection of relevant data such as measuring the depth of the river and a maximum flow rate for Sembrong Dam. The data were obtained from the Drainage and Irrigation Department Malaysia and the Department of Survey and Mapping and HLA Associates Sdn. Bhd. Then, the data were analyzed according to the established Info Works ICM method. The results found that the flooded area were listed at Sri Lalang, Parit Sagil, Parit Sonto, Sri Paya, Parit Raja, Parit Sempadan, Talang Bunut, Asam Bubok, Tanjung Sembrong, Sungai Rambut and Parit Haji Talib. Flood depth obtained for the related area started from 0.5 m up to 1.2 m. As a conclusion, the flood emanating from this study include the area around the town of Ayer Hitam up to Parit Raja approximately of more than 20 km distance. This may give bad implication to residents around these areas. In future studies, other rivers such as Sungai Batu Pahat should be considered for this study to predict and reduce the yearly flood victims for this area.