Neural Networks in Civil Engineering.  I: Principles and Understanding

Flood, Ian; Kartam, Nabil

doi:10.1061/(asce)0887-3801(1994)8:2(131)

Cited by 512 publications

(223 citation statements)

References 13 publications

Supporting

Mentioning

218

Contrasting

Unclassified

Order By: Relevance

“…Theoretically, an ANN with one hidden layer can approximate any function as long as sufficient neurons are used in the hidden layer (Hornik et al 1989). Flood and Kartam (1994) suggest using two hidden layers as a starting point. However, it must be stressed that optimal network geometry is highly problem dependent and therefore trial and error is in most cases the only option to determine the optimal number of hidden layers based on 'experience' with the dataset.…”

Section: Number Of Hidden Layersmentioning

confidence: 99%

Applications of artificial neural networks predicting macroinvertebrates in freshwaters

et al. 2007

View full text Add to dashboard Cite

To facilitate decision support in freshwater ecosystem protection and restoration management, habitat suitability models can be very valuable. Data driven methods such as artificial neural networks (ANNs) are particularly useful in this context, seen their time-efficient development and relatively high reliability. However, specialized and technical literature on neural network modelling offers a variety of model development criteria to select model architecture, training procedure, etc. This may lead to confusion among ecosystem modellers and managers regarding the optimal training and validation methodology. This paper focuses on the analysis of ANN development and application for predicting macroinvertebrate communities, a species group commonly used in freshwater assessment worldwide. This review reflects on the different aspects regarding model development and application based on a selection of 26 papers reporting the use of ANN models for the prediction of macroinvertebrates. This analysis revealed that the applied model training and validation methodologies can often be improved and moreover crucial steps in the modelling process are often poorly documented. Therefore, suggestions to improve model development, assessment and application in ecological river management are presented. In particular, data pre-processing determines to a high extent the reliability of the induced models and their predictive relevance. This also counts for the validation criteria, that need to be better tuned to the practical simulation requirements. Moreover, the use of sensitivity methods can help to extract knowledge on the habitat preference of species and allow peer-review by ecological experts. The selection of relevant input variables remains a critical challenge as well. Model coupling is a missing crucial step to link human activities, hydrology, physical habitat conditions, water quality and ecosystem status. This last aspect is probably the most valuable aspect to enable decision support in water management based on ANN models.

show abstract

Section: Number Of Hidden Layersmentioning

confidence: 99%

Applications of artificial neural networks predicting macroinvertebrates in freshwaters

et al. 2007

View full text Add to dashboard Cite

show abstract

“…network (ANN) models are able to solve highly nonlinear problems (KhasheiSiuki and Sarbazi, 2013). In the last two decades the application of ANNs in the field of hydrology and water resources has been documented in many papers (Flood and Kartam, 1994;Gupta et al, 2000;Kazemi and hosseini, 2011;yesilnacar and Sahinkaya, 2012;pektaş and doğan, 2015).…”

Section: Introductionmentioning

confidence: 99%

Spatial analysis of groundwater electrical conductivity using ordinary kriging and artificial intelligence methods (Case study: Tabriz plain, Iran)

et al. 2015

View full text Add to dashboard Cite

Artificial intelligence (AI) systems have opened a new horizon to analyze water engineering and environmental problems in recent decades. In this study performances of ordinary kriging (OK) as a linear geostatistical estimator and two intelligent methods including artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS) are investigated. For this purpose, geographical coordinates of 120 observation wells that located in Tabriz plain, north-west of Iran, were defined as inputs and groundwater electrical conductivities (EC) were set as output of models. Eighty percent of data were randomly selected to train and develop mentioned models and twenty percent of data used for testing and validating. Finally, the outputs of models were compared with the corresponding measured values in observation wells. Results indicated that ANFIS model provided the best accuracy among models with the root mean squared error (RMSE) value of 1.69 dS.m -1 and correlation coefficient (R) of 0.84. The RMSE values in ANN and OK were calculated 1.97 and 2.14 dS.m -1 and the R values were determined 0.79 and 0.76, respectively. According to the results, the ANFIS method predicted EC precisely and can be advised for modeling groundwater salinity.

show abstract

“…SPI can also detect moisture deficiency deeper than the PDSI, which has a response time interval of about 8-12 months [14]. An application of ANN to solve civil engineering problems began in the late 1980s [2]. The preliminary concepts of artificial neural networks (ANNs) and their adaptability to hydrology are well explained [3].…”

Section: Introductionmentioning

confidence: 99%

Application of Neural Modeling and the SPI Index for the Prediction of Weather Drought in the Saïss Plain (Northern Morocco)

Ibrahimi¹,

Baali²

2017

IJIES

View full text Add to dashboard Cite

This contribution will verify the effectiveness of formal neural networks for predicting drought in a semiarid region using a hybrid model of formal neural networks (ANN-MLP) and the standardized precipitation index ( SPI). Three types of models have been optimized to achieve this objective. A database consisting of SPI values, rain, temperature and potential evapotranspiration (PET) at the monthly time step was used as input for these models. These data have been standardized between 0 and 1 and subdivided into two blocks: a first block composed of 2/3 of the data for learning and a second block composed of 1/3 of the data for the test and the validation of the models. These models have been optimized with supervised learning. The activation function chosen is the logistic variant of the type sigmoid. The mean square error (RMSE), the correlation coefficient (R), the criterion of Nash-Sutcliffe (Nash) and the absolute mean error (MAE) were used to test the performance of these models. The results obtained show that the 3rd model is the most efficient. The application of neural networks for the estimation of the dryness of the Saïss Plain yielded quite good results. Indeed, the coefficients of correlation between the predicted and the measured values range from 0.63 to 0.97. It is therefore noted that the performances obtained are relatively good and could be improved by using a larger database.

show abstract

Neural Networks in Civil Engineering. I: Principles and Understanding

Cited by 512 publications

References 13 publications

Applications of artificial neural networks predicting macroinvertebrates in freshwaters

Applications of artificial neural networks predicting macroinvertebrates in freshwaters

Spatial analysis of groundwater electrical conductivity using ordinary kriging and artificial intelligence methods (Case study: Tabriz plain, Iran)

Application of Neural Modeling and the SPI Index for the Prediction of Weather Drought in the Saïss Plain (Northern Morocco)

Contact Info

Product

Resources

About