Predicting longitudinal dispersion coefficient using ANN with metaheuristic training algorithms

A reliable prediction of dispersion coefficient can provide valuable information for environmental scientists and river engineers as well. The main objective of this study is to apply intelligence techniques for predicting longitudinal dispersion coefficient in rivers. In this regard, artificial neural network (ANN) models were developed. Four different metaheuristic algorithms including genetic algorithm (GA), imperialist competitive algorithm (ICA), bee algorithm (BA) and cuckoo search (CS) algorithm were employed to train the ANN models. The results obtained through the optimization algorithms were compared with the Levenberg–Marquardt (LM) algorithm (conventional algorithm for training ANN). Overall, a relatively high correlation between measured and predicted values of dispersion coefficient was observed when the ANN models trained with the optimization algorithms. This study demonstrates that the metaheuristic algorithms can be successfully applied to make an improvement on the performance of the conventional ANN models. Also, the CS, ICA and BA algorithms remarkably outperform the GA and LM algorithms to train the ANN model. The results show superiority of the performance of the proposed model over the previous equations in terms of DR, R ² and RMSE.     

Development of roughness updating based on artificial neural network in a river hydraulic model for flash flood forecasting

Flood is the worst weather-related hazard in Taiwan because of steep terrain and storm. The tropical storm often results in disastrous flash flood. To provide reliable forecast of water stages in rivers is indispensable for proper actions in the emergency response during flood. The river hydraulic model based on dynamic wave theory using an implicit finite-difference method is developed with river roughness updating for flash flood forecast. The artificial neural network (ANN) is employed to update the roughness of rivers in accordance with the observed river stages at each time-step of the flood routing process. Several typhoon events at Tamsui River are utilized to evaluate the accuracy of flood forecasting. The results present the adaptive n-values of roughness for river hydraulic model that can provide a better flow state for subsequent forecasting at significant locations and longitudinal profiles along rivers.     

Theoretical analyses of nonaqueous phase liquid dissolution‐induced instability in two‐dimensional fluid‐saturated porous media

This paper deals with the theoretical aspects of nonaqueous phase liquid (NAPL)‐dissolution‐induced instability in two‐dimensional fluid‐saturated porous media including solute dispersion effects.After some weaknesses associated with the previous work are analyzed and overcome, a comprehensive dimensionless number, known as the Zhao number, is proposed to represent the main driving force and three controlling mechanisms of an NAPL‐dissolution system that has a finite domain. The linear stability analysis is carried out to derive the critical value of the comprehensive dimensionless number of the NAPL‐dissolution system in a limit case as the ratio of the equilibrium concentration to the density of the NAPL approaches zero. As a result, a theoretical criterion that can be used to assess the instability of planar NAPL‐dissolution fronts in two‐dimensional fluid‐saturated porous media of finite domains has been established. Not only can the present theoretical results be used for the theoretical understanding of the effect of solute dispersion on the instability of an NAPL‐dissolution front in the fluid‐saturated porous medium of either a finite domain or an infinite domain, but also they can be used as benchmark solutions for verifying numerical methods employed to simulate detailed morphological evolution processes of NAPL‐dissolution fronts in two‐dimensional fluid‐saturated porous media. Copyright © 2009 John Wiley & Sons, Ltd.     

Prediction of pile settlement using artificial neural networks based on standard penetration test data

In recent years artificial neural networks (ANNs) have been applied to many geotechnical engineering problems with some degree of success. With respect to the design of pile foundations, accurate prediction of pile settlement is necessary to ensure appropriate structural and serviceability performance. In this paper, an ANN model is developed for predicting pile settlement based on standard penetration test (SPT) data. Approximately 1000 data sets, obtained from the published literature, are used to develop the ANN model. In addition, the paper discusses the choice of input and internal network parameters which were examined to obtain the optimum model. Finally, the paper compares the predictions obtained by the ANN with those given by a number of traditional methods. It is demonstrated that the ANN model outperforms the traditional methods and provides accurate pile settlement predictions.     

Uncertainty analysis in bed load transport prediction of gravel bed rivers by ANN and ANFIS

River flow is a complex dynamic system of hydraulic and sediment transport. Bed load transport have a dynamic nature in gravel bed rivers and because of the complexity of the phenomenon include uncertainties in predictions. In the present paper, two methods based on the Artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS) are developed by using 360 data points. Totally, 21 different combination of input parameters are used for predicting bed load transport in gravel bed rivers. In order to acquire reliable data subsets of training and testing, subset selection of maximum dissimilarity (SSMD) method, rather than classical trial and error method, is used in finding randomly manipulation of these subsets. Furthermore, uncertainty analysis of ANN and ANFIS models are determined using Monte Carlo simulation. Two uncertainty indices of d factor and 95% prediction uncertainty and uncertainty bounds in comparison with observed values show that these models have relatively large uncertainties in bed load predictions and using of them in practical problems requires considerable effort on training and developing processes. Results indicated that ANFIS and ANN are suitable models for predicting bed load transport; but there are many uncertainties in determination of bed load transport by ANFIS and ANN, especially for high sediment loads. Based on the predictions and confidence intervals, the superiority of ANFIS to those of ANN is proved.     

Load-settlement behavior modeling of single piles using artificial neural networks and CPT data

Pile foundations are usually used when the conditions of the upper soil layers are weak and unable to support the super-structural loads. Piles carry these super-structural loads deep into the ground. Therefore, the safety and stability of pile-supported structures depends largely on the behavior of the piles. In addition, accurate prediction of pile behavior is necessary to ensure appropriate structural and serviceability performance. In this paper, an ANN model is developed for predicting pile behavior based on the results of cone penetration test (CPT) data. Approximately 500 data sets, obtained from the published literature, are used to develop the ANN model. The paper compares the predictions obtained by the ANN with those given by a number of traditional methods and it is observed that the ANN model significantly outperforms the traditional methods. An important advantage of the ANN model is that the complete load-settlement relationship is captured. Finally, the paper proposes a series of charts for predicting pile behavior that will be useful for pile design.     

An expert system based on hybrid ICA-ANN technique to estimate macerals contents of Indian coals

Coal, as an initial source of energy, requires a detailed investigation in terms of ultimate analysis, proximate analysis, and its biological constituents (macerals). The rank and calorific value of each type of coal are managed by the mentioned properties. In contrast to ultimate and proximate analyses, determining the macerals in coal requires sophisticated microscopic instrumentation and expertise. This study emphasizes the estimation of the concentration of macerals of Indian coals based on a hybrid imperialism competitive algorithm (ICA)–artificial neural network (ANN). Here, ICA is utilized to adjust the weight and bias of ANNs for enhancing their performance capacity. For comparison purposes, a pre-developed ANN model is also proposed. Checking the performance prediction of the developed models is performed through several performance indices, i.e., coefficient of determination (R ²), root mean square error and variance account for. The obtained results revealed higher accuracy of the proposed hybrid ICA-ANN model in estimating macerals contents of Indian coals compared to the pre-developed ANN technique. Results of the developed ANN model based on R ² values of training datasets were obtained as 0.961, 0.955, and 0.961 for predicting vitrinite, liptinite, and inertinite, respectively, whereas these values were achieved as 0.948, 0.947, and 0.957, respectively, for testing datasets. Similarly, R ² values of 0.988, 0.983, and 0.991 for training datasets and 0.989, 0.982, and 0.985 for testing datasets were obtained from developed ICA-ANN model.     

Mathematical expression of discharge capacity of compound open channels using MARS technique

In this paper, analytical methods, artificial neural network (ANN) and multivariate adaptive regression splines (MARS) techniques were utilised to estimate the discharge capacity of compound open channels (COC). To this end, related datasets were collected from literature. The results showed that the divided channel method with a coefficient of determination (R ²) value of 0.76 and root mean square error (RMSE) value of 0.162 has the best performance, among the various analytical methods tested. The performance of applied soft computing models with R ²=0.97 and RMSE = 0.03 was found to be more accurate than analytical approaches. Comparison of MARS with the ANN model, in terms of developed discrepancy ratio (DDR) index, showed that the accuracy of MARS model was better than that of MLP model. Reviewing the structure of the derived MARS model showed that the longitudinal slope of the channel (S), relative flow depth (H _r ) and relative area (A _r ) have a high impact on modelling and forecasting the discharge capacity of COCs.     

土工合成材料耐久性的神经网络预测

以BP人工神经网络为工具,利用其强大的非线性映射能力,在综合分析土工合成材料耐久性影响因素的基础上将土工合成材料所处环境的温度、湿度、紫外线照射情况以及土工合成材料的老化时间作为网络的输入参数,以描述土工合成材料耐久性状态的强度和延伸率作为网络的输出,建立了神经网络模型。采用大量试验数据对网络进行了训练和检验,并对土工合成材料的耐久性进行了预测。结果表明,预测值和试验结果比较接近,该网络能较好地反映土工合成材料耐久性与其影响因素之间的非线性映射关系。     

Modeling of tensile strength of rocks materials based on support vector machines approaches

In the predicting of geological variables, artificial neural networks (ANNs) have some drawbacks including possibility of getting trapped in local minima, over training, subjectivity in the determining of model parameters and the components of its complex structure. Recently, support vector machines (SVM) has been found to be popular in prediction studies due to its some advantages over ANNs. Because the least squares SVM (LS‐SVM) provides a computational advantage over SVM by converting quadratic optimization problem into a system of linear equations, LS‐SVM method is also tried in study. The main purpose of this study is to examine the capability of these two SVM algorithms for the prediction of tensile strength of rock materials and to compare its performance with ANN and linear regression (MLR) models. Total porosity, sonic velocity, slake durability index and aggregate impact value were used as input in modeling applications. Favorite performance evaluation measures were employed to assess developed models. The results determined in study indicate that the SVM, LS‐SVM and ANN methods are successful tools for prediction of tensile strength variable and can give good prediction performances than MLR model. Although these three methods are powerful artificial intelligence techniques, LS‐SVM makes the running time considerably faster with the higher accuracy. In terms of accuracy, the LS‐SVM model resulted in error reductions relative to that of the other models. Copyright © 2012 John Wiley & Sons, Ltd.     

Prediction of the Bullet Effect for Rockfall Barriers: a Scaling Approach

The so-called “bullet effect” refers to the perforation of a rockfall protection mesh by impact of a small block, which has a kinetic energy lower than the design value, where the design value is determined through tests with relatively large blocks. Despite playing a key role in the overall performance of a flexible rockfall barrier, this phenomenon is still poorly understood at present. An innovative approach for quantitatively characterizing this effect based on dimensional analysis is proposed in this paper. The analysis rests on a hypothesis that the relevant variables in the impact problem can be combined into three strongly correlated dimensionless parameters. The relationship between these dimensionless parameters (i.e., the scaling relationship) is subsequently investigated and validated by means of data generated with a finite element model. The validation process shows that the dimensionless parameters are apt and that the proposed scaling relationship characterizes the bullet effect with a reasonable level of accuracy. An example from the literature involving numerical simulation of a full rock barrier is considered, and satisfactory agreement between the calculated performance of the barrier and that predicted by the established scaling relationship is observed.     

河流纵向分散系数研究

研究了天然河流纵向分散系数理论公式及其参数的确定问题.借助于抛物型断面型态方程确定了河道垂线水深沿河宽的分布及流速分布对断面平均流速的偏离u‘的横向分布,给出了横向混合系数计算方法.在此基础上通过对Fischer的三重积分的直接求解,建立了新的天然河流纵向分散系数计算公式.这一新建立的纵向分散系数计算公式与原有的有代表性的经验公式以及26条美国河流上实测的59组资料进行了比较,比较结果表明,本文建立的纵向分散系数计算公式能给出与实测纵向分散系数最接近的预测值.与现有的其它纵向分散系数计算公式相比,本文建立的天然河流纵向分散系数公式理论上更加合理,机理上更加清楚,并且具有最小的预测误差.     

Neural network prediction of the reliability of heterogeneous cohesive slopes

The reliability of heterogeneous slopes can be evaluated using a wide range of available probabilistic methods. One of these methods is the random finite element method (RFEM), which combines random field theory with the non‐linear elasto‐plastic finite element slope stability analysis method. The RFEM computes the probability of failure of a slope using the Monte Carlo simulation process. The major drawback of this approach is the intensive computational time required, mainly due to the finite element analysis and the Monte Carlo simulation process. Therefore, a simplified model or solution, which can bypass the computationally intensive and time‐consuming numerical analyses, is desirable. The present study investigates the feasibility of using artificial neural networks (ANNs) to develop such a simplified model. ANNs are well known for their strong capability in mapping the input and output relationship of complex non‐linear systems. The RFEM is used to generate possible solutions and to establish a large database that is used to develop and verify the ANN model. In this paper, multi‐layer perceptrons, which are trained with the back‐propagation algorithm, are used. The results of various performance measures indicate that the developed ANN model has a high degree of accuracy in predicting the reliability of heterogeneous slopes. The developed ANN model is then transformed into relatively simple formulae for direct application in practice. Copyright © 2016 John Wiley & Sons, Ltd.     

Three‐dimensional responses of buried corrugated pipes and ANN‐based method for predicting pipe deflections

This study investigated localized responses, such as circumferential stresses, on corrugation and pipe deflections. Also, this study examined the effect of corrugation geometry on the overall and localized response of corrugated pipes with refined three‐dimensional modeling of the entire soil–pipe interaction system, including corrugation. To investigate the availability of the traditional two‐dimensional method, the results from the three‐dimensional finite element method (FEM) were compared with those from the two‐dimensional FEM. The soil–pipe modeling techniques of this study were verified by comparing the FEM results by Utah State University and analytical results. An artificial neural network (ANN)‐based model to predict vertical deflections of buried corrugated pipes was developed to overcome the shortcomings of existing methods and obtain results that are close to the level of accuracy of FEM results. In order to train an ANN, analyses on a large amount of data were executed with various standardized pipe geometries and burial depths regulated by the Korea Highway Corporation using the two‐dimensional FEM verified in this study. The widely used back propagation algorithm was adopted. The ANN‐based model developed in this study was shown to be an effective tool by comparing the results with test data and sensitivity analyses were executed based on the data from the developed ANN. Copyright © 2010 John Wiley & Sons, Ltd.     

西宁市贵德县河滨公园林场第四系含水层弥散试验研究

地下水污染问题日益严重,研究溶质运移的弥散理论开始应用于实际问题。建立地下水溶质运移模型,对地下水中污染物的运移及发展趋势进行准确预测,是对地下水进行保护、对地下水污染进行控制的基础。而弥散参数的确定则是地下水溶质运移模型建立的关键环节之一,直接影响着模型预测结果的精度和准确性。 对西宁市贵德县地下水污染的水质运移规律进行分析,在贵德县河滨公园林场采用径向收敛流水动力弥散理论方法进行了第四系含水层现场弥散试验,计算了试验场地潜水含水层的弥散度,获得纵向弥散度(a_L)为0.843~0.998 cm,横向弥散度(a_T)经验推断值为0.17~0.20 cm,为进一步建立该地区的地下水溶质运移模型、预测地下水污染的发展趋势和评价该地区地下水环境质量提供了数据参考。     

Application of neural network and MODIS 250m imagery for estimating suspended sediments concentration in Hangzhou Bay,China

Suspended sediments concentration (SSC) in surface water derived from bottom sediment resuspension or discharge of sediment-laden rivers is an important indication of coastal water quality and changes rapidly in high-energy coastal area. Since artificial neural networks (ANN) had been proven successful in modeling a variety of geophysical transfer functions, an ANN model to simulate the relationship between surface water SSC and satellite-received radiances was employed. In situ SSC measurements from the Hangzhou Bay and the Moderate-resolution Imaging Spectroradiometer (MODIS) 250 m daily products were adopted in this study. Significant correlations were observed between in situ measurements and band 1–2 reflectance values of MODIS images, respectively. Results indicated that application of ANN model with one hidden layer appeared to yield superior simulation performance (r ² = 0.98; n = 25) compared with regression analysis method. The RMSE for the ANN model was less than 10%, whereas the RMSE for the regression analysis was more than 25%. Results also showed that different tidal situations affect the model simulation results to some extent. The SSC of surface water in Hangzhou Bay is high and changes rapidly due to tidal flood and ebb during a tidal cycle. The combined utilization of Terra and Aqua MODIS data can capture the tidal cycle induced dynamic of surface water SSC. This study demonstrated that MODIS 250 m daily products and ANN model are useful for monitoring surface SSC dynamic within high-energy coastal water environments.     

A Bayesian network approach for predicting seismic liquefaction based on interpretive structural modeling

The Bayesian network (BN) is a type of graphical network based on probabilistic inference that has been gradually applied to assessment of seismic liquefaction potential. However, how to construct a robust BN remains underexplored in this field. This paper aims to present an efficient hybrid approach combining domain knowledge and data to construct a BN that facilitates the integration of multiple factors and the quantification of uncertainties within a network model to assess seismic liquefaction. Initially, only using given domain knowledge, a naive network model can be constructed using interpretive structural modeling. Thereafter, some effective information about the naive model is provided to construct a robust model using structural learning of BN from historic data. Finally, the returning predictive results and the predictive results are compared to other methods including non-probabilistic and probabilistic models for seismic liquefaction using the metrics of the overall accuracy, the area under the curve of receiver operating characteristic, prediction, recall and F₁ score. The methodology proposed in this paper achieved better performance, and we discussed the power and value of the proposed approach at the end of this paper, which suggest that BN is a good alternative tool for seismic liquefaction prediction.     

Prediction of relative crest settlement of concrete-faced rockfill dams analyzed using an artificial neural network model

A neural network model has been developed for the prediction of relative crest settlement (RCS) of concrete-faced rockfill dams (CFRDs) using 30 databases of field data from seven countries (of which 21 were used for training and 9 for testing). The settlement values predicted using the optimum artificial neural network (ANN) model are in good agreement with these field data. A database prepared from reported crest settlement values of CFRDs after construction was used to train the ANN model to predict the RCS. It is demonstrated here that the model is capable of predicting accurately the relative crest settlement of CFRDs and is potentially applicable for general usage with knowledge of the three basic properties of a dam (void ratio, e; height, H; and vertical deformation modulus, E_V).

The performance of the new ANN model is compared with that of conventional methods based on the Clements theory and also with that of a proposed equation derived from the field data. The comparison indicates that the ANN model has strong potential and offers better performance than conventional methods when used as a quick interpolation and extrapolation tool. The conventional calculation model was proposed based on the fixed connection weights and bias factors of the optimum ANN structure. This method can support the dam engineer in predicting the relative crest settlement of a CFRD after impounding.     

Predicting discharge coefficient of compound broad-crested weir by using genetic programming (GP) and artificial neural network (ANN) techniques

Compound broad-crested weir is a typical hydraulic structure that provides flow control and measurements at different flow depths. Compound broad-crested weir mainly consists of two sections; first, relatively small inner rectangular section for measuring low flows, and a wide rectangular section at higher flow depths. In this paper, series of laboratory experiments was performed to investigate the potential effects of length of crest in flow direction, and step height of broad-crested weir of rectangular compound cross-section on the discharge coefficient. For this purpose, 15 different physical models of broad-crested weirs with rectangular compound cross-sections were tested for a wide range of discharge values. The results of examination for computing discharge coefficient were yielded by using multiple regression equations based on the dimensional analysis. Then, the results obtained were also compared with genetic programming (GP) and artificial neural network (ANN) techniques to investigate the applicability, ability, and accuracy of these procedures. Comparison of results from the GP and ANN procedures clearly indicates that the ANN technique is less efficient in comparison with the GP algorithm, for the determination of discharge coefficient. To examine the accuracy of the results yielded from the GP and ANN procedures, two performance indicators (determination coefficient (R ²) and root mean square error (RMSE)) were used. The comparison test of results clearly shows that the implementation of GP technique sound satisfactory regarding the performance indicators (R ²?=?0.952 and RMSE?=?0.065) with less deviation from the numerical values.     

Landslide susceptibility mapping at Zonouz Plain,Iran using genetic programming and comparison with frequency ratio,logistic regression,and artificial neural network models

Without a doubt, landslide is one of the most disastrous natural hazards and landslide susceptibility maps (LSMs) in regional scale are the useful guide to future development planning. Therefore, the importance of generating LSMs through different methods is popular in the international literature. The goal of this study was to evaluate the susceptibility of the occurrence of landslides in Zonouz Plain, located in North-West of Iran. For this purpose, a landslide inventory map was constructed using field survey, air photo/satellite image interpretation, and literature search for historical landslide records. Then, seven landslide-conditioning factors such as lithology, slope, aspect, elevation, land cover, distance to stream, and distance to road were utilized for generation LSMs by various models: frequency ratio (FR), logistic regression (LR), artificial neural network (ANN), and genetic programming (GP) methods in geographic information system (GIS). Finally, total four LSMs were obtained by using these four methods. For verification, the results of LSM analyses were confirmed using the landslide inventory map containing 190 active landslide zones. The validation process showed that the prediction accuracy of LSMs, produced by the FR, LR, ANN, and GP, was 87.57, 89.42, 92.37, and 93.27 %, respectively. The obtained results indicated that the use of GP for generating LSMs provides more accurate prediction in comparison with FR, LR, and ANN. Furthermore; GP model is superior to the ANN model because it can present an explicit formulation instead of weights and biases matrices.