Predicting typhoon-induced storm surge tide with a two-dimensional hydrodynamic model and artificial neural network model

Hurricane storm surge represents a significant threat to coastal communities around the world. Here, we use artificial neural network (ANN) models to predict storm surge levels using hurricane characteristics along the US Gulf and East Coasts. The ANN models are trained with storm surge levels from a hydrodynamic model and physical characteristics of synthetic hurricanes which are downscaled from National Centers for Environmental Prediction (NCEP) reanalysis using a statistical‐deterministic hurricane model. The ANN models are able to accurately predict storm surge levels with root‐mean‐square errors (RMSE) below 0.2 m and correlation coefficients > 0.85. The ANN models trained with the NCEP data also show satisfactory accuracy (RMSE below 0.7 m; correlation > 0.7) in predicting storm surge levels for hurricanes downscaled from future climate projections. Once trained, we use the ANN models to assess the sensitivity of storm surge levels to variations in hurricane characteristics and local geophysical features. Progressively stronger maximum wind speeds and larger outer radius sizes independently increase storm surge levels at all locations along the US East and Gulf Coasts. The response of storm surge levels to changes in hurricane translation speed, however, is found to be sensitive to coastal configuration, with increases in hurricane translation speed amplifying (reducing) storm surge levels in open ocean (semi‐enclosed) regions.

The applicability of artificial neural networks (ANNs) and the adaptive neuro-fuzzy inference system (ANFIS) for determination of mean velocity and discharge of natural streams is investigated. The 2,184 field data obtained from four different sites on the Sarimsakli and Sosun streams in central Turkey were used in the study. ANNs and ANFIS models use the inputs, water surface velocity and water surface slope, to estimate the mean velocity and discharges of natural streams. The accuracies of both models were compared with the multiple-linear regression (MLR) model. The comparison results showed that the ANFIS model performed better than the ANNs and regression models for estimating mean velocity and discharge. The ANN model also showed better accuracy than the MLR model. The root mean square errors (RMSE) and mean absolute relative errors (MARE) of the MLR model were reduced by 88 and 91 % using the ANFIS model in estimating discharges, respectively. It is found that the optimal ANFIS model with RMSE of 0,063, MARE of 3,47 and determination coefficient (R2) of 0,996 in the test period is superior in estimation of discharge than the MLR model with RMSE of 0,532, MARE of 38,9 and R2 of 0,776, respectively. The study reveals that the ANFIS technique can be successfully used for estimating the mean velocity and discharge of natural streams by using only the inputs of water surface velocity and water surface slope.

Laboratory experiments were used to estimate the hydraulic performance of emitters, i.e., the emitter flow variation (qvar) and manufacturer’s coefficient of variation (CVm), by measuring the discharge of different labyrinth-channel emitters at different operating pressures (P) and water temperatures (T). Considering the importance of the structural parameters of the labyrinth-channel emitters in drip irrigation design, which has been experimentally confirmed, artificial neural network (ANN) and gene expression programming (GEP) models were developed to predict qvar and CVm. The ANN and GEP models were trained and tested using structural parameters (including the number, height (H), and spacing of trapezoidal units and the flow path width and length) of different labyrinth-channel emitters, P and T as the input variables, and qvar and CVm as the outputs. Statistical criteria, including the coefficients of correlation (r), relative root-mean-square error (RRMSE), and mean absolute error (MAE), were used to examine the accuracy of the developed models. The ANN models exhibited good correlation with experimental values, with high r values 0.995 and 0.969 for qvar and 0.997 and 0.947 for CVm in the training and testing processes, respectively. The ANN models had lower RRMSE and MAE values than the GEP models. Furthermore, H was the dominant variable for obtaining the most accurate prediction model. The results confirm that the ANN models are superior to the GEP models for the prediction of the hydraulic performance of emitters.

Evaluation of ANN and ANFIS modeling ability in the prediction of AISI 1050 steel machining performance

Local scour around bridge piers is a complicated physical process and involves highly three-dimensional flows. Thus, the scour depth, which is directly related to the safety of a bridge, cannot be given in the form of the exact relationship of dependent variables via an analytical method. This paper proposes the use of the adaptive neuro-fuzzy inference system (ANFIS) method for predicting the scour depth around a bridge pier. Five variables including mean velocity, flow depth, size of sediment particles, critical velocity for particles' initiation of motion, and pier width were used for the scour depth. For comparison, predictions by the artificial neural network (ANN) model were also provided. Both the ANN model and ANFIS method were trained and validated. The findings indicate that the modeling with dimensional variables yields better predictions than when normalized variables are used. The ANN model was applied to a field-scale dataset. Prediction results indicated that the errors are much larger compared to the case of a laboratory-scale dataset. The MAPE by the ANN model trained with part of the field data was not seriously different from that by the model trained with the laboratory data. However, the application of the ANFIS method improved the predictions significantly, reducing the MAPE to the half of that by the ANN model. Five selected empirical formulas were also applied to the same dataset, and Sheppard and Melville's formula was found to provide the best prediction. However, the MAPEs for the scour depths predicted by empirical formulas are much larger than MAPEs by either the ANN or the ANFIS method. The ANFIS method predicts much better if the range of the training dataset is sufficiently wide to cover the range of the application dataset.

Development of regional groundwater model isan important phase to understand the mechanism of groundwater resource and its sustainability. This model is extensively restricted by the requirement of large number of field data and specific interconnected constraints. A choice of intelligent soft computing technique is always constantly open for such model development. Recently ANN (Artificial Neural Network) and ANFIS (Adaptive Neuro Fuzzy Inference System) techniques are having a massive strength to deal with such interconnected constrains model. The present study is carried outto perform the comparative prediction between ANN, ANFIS, CWTFT-ANN (Continuous Wavelet Fast Fourier Transform), CWTFT-ANFIS, WT-ANN (Wavelet Transform) and WT-ANFIS on Groundwater Level prediction at different reaches (Top, Middle and End) of Lower Bhavani River Basin (LBRB) on monthly stress basis(from 2009 to 2015). From the results, the performance of CWTFT-ANFIS is 9.36%, 13.3% and 4.45% better than the ANFIS prediction and the performance of CWTFT-ANN is 47.17%, 25.6% and 47.45% better than the ANN prediction at Top, Middle and End reaches of LBRB respectively. Overall the prediction of CWTFT-ANFIS is about15.3% better than the other identified models, which is further fed for the forecasting of groundwater level to next one time stress level.

A Comparative Study on Prediction of Monthly Streamflow Using Hybrid ANFIS-PSO Approaches

Case study of performance evaluation of ground source heat pump system based on ANN and ANFIS models

Artificial intelligence-based estimation of flushing half-cone geometry

Performance of radial basis and LM-feed forward artificial neural networks for predicting daily watershed runoff

ANN and ANFIS models for performance evaluation of a vertical ground source heat pump system

Accurate modelling of the drying process of agricultural material is an essential step toward the development of advanced drying monitoring and control systems. In this study, Artificial Neural Networks (ANNs), Adaptive Neuro-Fuzzy Inference Systems (ANFIS) and mathematical modelling were employed to predict the moisture ratio of paddy during the drying process in a combined hot air IR-assisted dryer in fixed and vibratory bed modes. The grains were dried as thin layers at three infrared power levels of 50, 100 and 150 W and temperature levels of 40, 50 and 60 °C. The simulation results proved that The ANN model with 3-18-16-1 topology, LM training function, logarithm sigmoid transfer function in the first, and tangent sigmoid transfer function in the second hidden layer was the most accurate model. The R2, RMSE and MAE values for the ANN model in the fixed-bed mode were 99.92%, 0.0037 and 0.0026, respectively. These values for vibratory mode were 99.94%, 0.0030 and 0.0021, respectively. It was concluded that ANN and ANFIS models were more appropriate tools than mathemati­cal models for predicting the hot air-IR drying kinetics of paddy.

ANN and ANFIS performance prediction models for hydraulic impact hammers

OuJiang River estuary and WenZhou bay are located in the southeast coast of Zhejiang province, China. At OuJiang river estuary, the storm surges are usually accompanied with flood. In this paper, the one-dimensional and two-dimensional coupling flood and storm surge model of the OuJiang river estuary and East China Sea area is used to examine the flood and storm surge dynamics in the region. The one-dimensional model is used for simulating the flood of the upstream of OuJiang River, the unstructured two-dimensional finite volume model is used for simulating the storm surge. The one-dimensional model and two-dimensional model are coupling at the estuary. The storm surges caused by 199417, 199608 and 200505 typhoons are simulated and the surge elevations at the nearshore, estuary and river are compared with the field observation results, which show that the coupling model can be good simulated the flood and storm surge process.

Prediction of storm surge and coastal inundation using Artificial Neural Network – A case study for 1999 Odisha Super Cyclone