Novel Time Series Bagging Based Hybrid Models for Predicting Historical Water Levels in the Mekong Delta Region, Vietnam

Water level predictions in the river,lake and delta play an important role in flood management.Every year Mekong River delta of Vietnam is experiencing flood due to heavy monsoon rains and high tides.Land subsidence may also aggravate flooding problems in this area.Therefore,accurate predictions of water levels in this region are very important to forewarn the people and authorities for taking timely adequate remedial measures to prevent losses of life and property.There are so many methods available to predict the water levels based on historical data but nowadays Machine Learning(ML)methods are considered the best tool for accurate prediction.In this study,we have used surface water level data of 18 water level measurement stations of the Mekong River delta from 2000 to 2018 to build novel time-series Bagging based hybrid ML models namely:Bagging(RF),Bagging(SOM)and Bagging(M5P)to predict historical water levels in the study area.Performances of the Bagging-based hybrid models were compared with Reduced Error Pruning Trees(REPT),which is a benchmark ML model.The data of 19 years period was divided into 70:30 ratio for the modeling.The data of the period 1/2000 to 5/2013(which is about 70%of total data)was used for the training and for the period 5/2013 to 12/2018(which is about 30%of total data)was used for testing(validating)the models.Performance of the models was evaluated using standard statistical measures:Coefficient of Determination(R2),Root Mean Square Error(RMSE)and Mean Absolute Error(MAE).Results show that the performance of all the developed models is good(R2>0.9)for the prediction of water levels in the study area.However,the Bagging-based hybrid models are slightly better than another model such as REPT.Thus,these Bagging-based hybrid time series models can be used for predicting water levels at Mekong data.

Novel hybrid models of ANFIS and metaheuristic optimizations (SCE and ABC) for prediction of compressive strength of concrete using rebound hammer field test

In this study,we developed novel hybrid models namely Adaptive Neuro Fuzzy Inference System(ANFIS)optimized by Shuffled Complex Evolution(SCE)on the one hand and ANFIS with Artificial Bee Colony(ABC)on the other hand.These were used to predict compressive strength(Cs)of concrete relating to thirteen concrete-strength affecting parameters which are easy to determine in the laboratory.Field and laboratory tests data of 108 structural elements of 18 concrete bridges of the Ha Long-Van Don Expressway,Vietnam were considered.The dataset was randomly divided into a 70:30 ratio,for training(70%)and testing(30%)of the hybrid models.Performance of the developed fuzzy metaheuristic models was evaluated using standard statistical metrics:Correlation Coefficient(R),Root Mean Square Error(RMSE)and Mean Absolute Error(MAE).The results showed that both of the novel models depict close agreement between experimental and predicted results.However,the ANFIS-ABC model reflected better convergence of the results and better performance compared to that of ANFIS-SCE in the prediction of the concrete Cs.Thus,the ANFIS-ABC model can be used for the quick and accurate estimation of compressive strength of concrete based on easily determined parameters for the design of civil engineering structures including bridges.