Flooding is becoming a prominent issue in coastal cities, flood forecasting is the key to solving this problem. However, the lack and imbalance of research data and the insufficient performance of the model have led to the complexity and uncontrollability of flood forecasting. To forecast coastal floods accurately and reliably, the Internet of Things technology is used to collect data on floods and flood factors in smart cities. An ensemble learning method based on Bayesian model combination (BMC-EL) is designed to predict flood depth. First, flood intensity classification and K-fold cross-validation are introduced to generate multiple training subsets from the training set to realize uniform sampling and increase the diversity of subsets. Second, the backpropagation neural network (BPNN) and random forest (RF) are used as the base learners to build the prediction model and then import it into training subsets for training purposes. Finally, based on the prediction performance of the base learner in the validation sets, the Bayesian model combination strategy is formulated to integrate and output predicted values. We describe experiments conducted to forecast flood depth 1 h in advance that several machine learning models were trained and tested using real flood data taken from Macao, China. The models include linear regression, support vector machine, BPNN, RF and BMC-EL models. Results prove the accuracy and reliability of the BMC-EL method in flood forecasting for coastal cities.

Using data-driven models to predict floods in advance is one of the current effective methods and hot researches to reduce urban flood disasters. In order to improve the prediction accuracy, it is necessary to select the appropriate flood hazard factors and the number of training samples to construct the prediction model. In our current research, an artificial neural network (i.e., the back-propagation neural network, BPNN) model was developed to predict the flood depth in the next hour. A case study of the urban flood during six typhoons in Macau of China was conducted to prove the performance of the proposed model. The flood depth was collected as output; after analyzing their correlation to the flood typhoon optimum track, urban weather, tides, geographic height and water depth increment of the submerged area were used as input. As a result, four models trained with different sample numbers were developed for training and testing. The model performances were examined using average absolute error, root mean square error and the coefficient of determination. The results show that in this case study, the 30-min scale model provides reliable predictions and can provide useful decision support for the prevention and mitigation of flood disasters in coastal urban.

Climate change causes extreme weather in Macao, especially typhoons and flooding. In this paper, some raw flood data is missing from the Macao Meteorological and Geophysical Bureau, due to some flood sensors that were damaged during Typhoon Hato in 2017 and Typhoon Mangkhut in 2018. So we use data interpolation to construct new datasets and curve fitting to simulate real inundation depth. Besides this, we explore Neural Network, Long Short-Term Memory, Random Forest, Adaptive Boosting, and Linear Regression for analyzing, comparing, and evaluating the best combinations of flood prediction models, datasets, and scenarios caused by typhoon presence in Macao. Furthermore, we apply Bayes Network to the aforementioned models to evaluate the accuracy of predicting flood situations because of typhoons. The experiment results show that the different models achieve a different performance in predicting specific scenarios.