Ensemble Learning Technology for Coastal Flood Forecasting in Internet-of-Things-Enabled Smart City

Abstract

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.

Flood Prediction System Using IOT & Artificial Neural Network

“…Regression trees (RT) are used when the objective variable has continuous values, and a group of trees is employed. DTs are fast algorithms, making them popular for flood modeling and forecasting [22].…”

Big Data Challenges and Opportunities for Disaster Early Warning System

“…For instance, the system, called the Macao Flood Information Management System (MFIMS), was developed by the Macao Water Supply Company, and uses big data and AI technology to predict flooding caused by typhoons. This system uses big data to push emergency alerts as early warning and help emergency responders better allocate resources during a typhoon [16][17][18]. In the context of disaster risk management, early warning systems can make use of such big data to mitigate the risks effectively.…”

Assessing the Implication of Climate Change to Forecast Future Flood Using CMIP6 Climate Projections and HEC-RAS Modeling

“…Furthermore, for bias correction and downscaling of the dataset, many more methods can be employed to deal with complex topographic features, unpredictable hydrological variations, and biases in climate model datasets. Past research has explored other procedures in terms of the forecasting of hydrological parameters such as Gravity Recovery and Climate Experiment (GRACE) [71], machine learning algorithms [72,73], and Internet of Things (IoT) [74,75]. A comparison of these measures with the CMIP6 model could be performed for further validation of the climate model and betterment in the forecasting study.…”