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.

A state of art review on time series forecasting with machine learning for environmental parameters in agricultural greenhouses

Agricultural greenhouse production has to require a stable and acceptable environment,it is therefore essential for future greenhouse production to obtain full and precisely internal dynamic environment parameters.Dynamic modeling based on machine learning methods,e.g.,intelligent time series prediction modeling,is a popular and suitable way to solve the above issue.In this article,a systematic literature review on applying advanced time series models has been systematically conducted via a detailed analysis and evaluation of 61 pieces selected from 221 articles.The historical process of time series model application from the use of data and information strategies was first discussed.Subsequently,the accuracy and generalization of the model from the selection of model parameters and time steps,providing a new perspective for model development in this field,were compared and analyzed.Finally,the systematic review results demonstrate that,compared with traditional models,deep neural networks could increase data structure mining capabilities and overall information simulation capabilities through innovative and effective structures,thereby it could also broaden the selection range of environmental parameters for agricultural facilities and achieve environmental prediction end-to-end optimization via intelligent time series model based on deep neural networks.