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| 1 | Prediction of Flash Flood Susceptibility of Hilly Terrain Using Deep Neural Network:A Case Study of Vietnam显示文摘Flash floods are one of the most dangerous natural disasters,especially in hilly terrain,causing loss of life,property,and infrastructures and sudden disruption of traffic.These types of floods are mostly associated with landslides and erosion of roads within a short time.Most of Vietnamis hilly and mountainous;thus,the problem due to flash flood is severe and requires systematic studies to correctly identify flood susceptible areas for proper landuse planning and traffic management.In this study,three Machine Learning(ML)methods namely Deep Learning Neural Network(DL),Correlation-based FeatureWeighted Naive Bayes(CFWNB),and Adaboost(AB-CFWNB)were used for the development of flash flood susceptibility maps for hilly road section(115 km length)of National Highway(NH)-6 inHoa Binh province,Vietnam.In the proposedmodels,88 past flash flood events were used together with 14 flash floods affecting topographical and geo-environmental factors.The performance of themodels was evaluated using standard statisticalmeasures including Receiver Operating Characteristic(ROC)Curve,Area Under Curve(AUC)and Root Mean Square Error(RMSE).The results revealed that all the models performed well(AUC>0.80)in predicting flash flood susceptibility zones,but the performance of the DL model is the best(AUC:0.972,RMSE:0.352).Therefore,the DL model can be applied to develop an accurate flash flood susceptibility map of hilly terrain which can be used for proper planning and designing of the highways and other infrastructure facilities besides landuse management of the area. | Huong Thi Thanh Ngo Nguyen Duc Dam Quynh-Anh Thi Bui Nadhir Al-Ansari Romulus Costache Hang Ha Quynh Duy Bui Sy Hung Mai Indra Prakash Binh Thai Pham | 2023 | Computer Modeling in Engineering & Sciences2023，，6： | 2 |
| 2 | Novel Time Series Bagging Based Hybrid Models for Predicting Historical Water Levels in the Mekong Delta Region, Vietnam显示文摘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. | Nguyen Thanh Hoan Nguyen Van Dung Ho Le Thu Hoa Thuy Quynh Nadhir Al-Ansari Tran Van Phong Phan Trong Trinh Dam Duc Nguyen Hiep Van Le Hanh Bich Thi Nguyen Mahdis Amiri Indra Prakash Binh Thai Pham | 2022 | Computer Modeling in Engineering & Sciences2022，，6： | 0 |
| 3 | Prediction of falling weight deflectometer parameters using hybrid model of genetic algorithm and adaptive neuro-fuzzy inference system显示文摘A falling weight deflectometer is a testing device used in civil engineering to measure and evaluate the physical properties of pavements,such as the modulus of the subgrade reaction(Y1)and the elastic modulus of the slab(Y2),which are crucial for assessing the structural strength of pavements.In this study,we developed a novel hybrid artificial intelligence model,i.e.,a genetic algorithm(GA)-optimized adaptive neuro-fuzzy inference system(ANFIS-GA),to predict Y1 and Y2 based on easily determined 13 parameters of rigid pavements.The performance of the novel ANFIS-GA model was compared to that of other benchmark models,namely logistic regression(LR)and radial basis function regression(RBFR)algorithms.These models were validated using standard statistical measures,namely,the coefficient of correlation(R),mean absolute error(MAE),and root mean square error(RMSE).The results indicated that the ANFIS-GA model was the best at predicting Y1(R=0.945)and Y2(R=0.887)compared to the LR and RBFR models.Therefore,the ANFIS-GA model can be used to accurately predict Y1 and Y2 based on easily measured parameters for the appropriate and rapid assessment of the quality and strength of pavements. | Long Hoang NGUYEN Dung Quang VU Duc Dam NGUYEN Fazal E.JALAL Mudassir IQBAL Vinh The DANG Hiep Van LE Indra PRAKASH Binh Thai PHAM | 2023 | Frontiers of Structural and Civil Engineering2023，17，5： | 0 |
| 4 | Biological Community in Submerged Caves and Marine Lakes in ha Long-Cat Ba Area, Vietnam显示文摘 | Nguyen Dang Ngai Dau Van Thao Do Cong Thung Le Thi Thuy Dam Duc Tien Nguyen Van Quan PhamVan Chien | 2015 | Journal of Life Sciences2015，9，11： | 0 |
| 5 | Novel hybrid models of ANFIS and metaheuristic optimizations (SCE and ABC) for prediction of compressive strength of concrete using rebound hammer field test显示文摘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. | Dung Quang VU Fazal EJALAL Mudassir IQBAL Dam Duc NGUYEN Duong Kien TRONG Indra PRAKASH Binh Thai PHAM | 2022 | Frontiers of Structural and Civil Engineering2022，16，8： | 0 |
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