scholarly journals Modeling soil erosion by data-driven methods using limited input variables

2017 ◽  
Vol 49 (5) ◽  
pp. 1349-1362 ◽  
Author(s):  
Shahla Yavari ◽  
Saman Maroufpoor ◽  
Jalal Shiri
Keyword(s):  
Soil Erosion ◽  
Data Driven ◽  
Coefficient Of Determination ◽  
Accurate Estimation ◽  
Neuro Fuzzy ◽  
Upland Erosion ◽  
Ann Models ◽  
Input Variables ◽  
Artificial Neural Network Ann ◽  
Development And Management

Abstract Soil is one of the main elements of natural resources. Accurate estimation of soil erosion is very important in optimum soil resources development and management. Analyzing soil erosion by water on cultivated lands is an important task due to the numerous problems caused by erosion. In this study, the performance of three different data-driven approaches, e.g. multilayer perceptron artificial neural network (ANN), grid partitioning (GP), and subtractive neuro-fuzzy (NF) models were evaluated for estimating soil erosion. Land use, slope, soil and upland erosion amount were used as input parameters of the applied models and the erosion values obtained by MPSIAC method were considered as the benchmark for evaluating the ANN and NF models. The applied models were assessed using the coefficient of determination (R2), the root mean square error (RMSE), the BIAS, and the variance accounted for (VAF) indices. The results showed that the subtractive NF model presented the most accurate results with the minimum RMSE value (3.775) and GP, NF and ANN models were ranked successively.

scholarly journals Monthly groundwater level prediction using ANN and neuro-fuzzy models: a case study on Kerman plain, Iran

2010 ◽  
Vol 13 (4) ◽  
pp. 867-876 ◽  
Author(s):  
Amir Jalalkamali ◽  
Hossein Sedghi ◽  
Mohammad Manshouri
Keyword(s):  
Arid Regions ◽  
Groundwater Resources ◽  
Good Choice ◽  
Groundwater Levels ◽  
Fuzzy Models ◽  
Neuro Fuzzy ◽  
Ann Models ◽  
Artificial Neural Network Ann ◽  
Groundwater Level Prediction

The prediction of groundwater levels in a well has immense importance in the management of groundwater resources, especially in arid regions. This paper investigates the abilities of neuro-fuzzy (NF) and artificial neural network (ANN) techniques to predict the groundwater levels. Two different NF and ANN models comprise various combinations of monthly variablities, that is, air temperature, rainfall and groundwater levels in neighboring wells. The result suggests that the NF and ANN techniques are a good choice for the prediction of groundwater levels in individual wells. Also based on comparisons, it is found that the NF computing techniques have better performance than the ANN models in this case.

scholarly journals Modeling of hourly river water temperatures using artificial neural networks

2014 ◽  
Vol 49 (2) ◽  
pp. 144-162 ◽  
Author(s):  
Cindie Hebert ◽  
Daniel Caissie ◽  
Mysore G. Satish ◽  
Nassir El-Jabi
Keyword(s):  
Water Temperature ◽  
Coefficient Of Determination ◽  
Ann Model ◽  
Air Temperatures ◽  
Level Data ◽  
Water Level Data ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
The Many

Water temperature is an important component for water quality and biotic conditions in rivers. A good knowledge of river thermal regime is critical for the management of aquatic resources and environmental impact studies. The objective of the present study was to develop a water temperature model as a function of air temperatures, water temperatures and water level data using artificial neural network (ANN) techniques for two thermally different streams. This model was applied on an hourly basis. The results showed that ANN models are an effective modeling tool with overall root-mean-square-error of 0.94 and 1.23 °C, coefficient of determination (R2) of 0.967 and 0.962 and bias of −0.13 and 0.02 °C, for Catamaran Brook and the Little Southwest Miramichi River, respectively. The ANN model performed best in summer and autumn and showed a poorer performance in spring. Results of the present study showed similar or better results to those of deterministic and stochastic models. The present study shows that the predicted hourly water temperatures can also be used to estimate the mean and maximum daily water temperatures. The many advantages of ANN models are their simplicity, low data requirements, their capability of modeling long-term time series as well as having an overall good performance.

Artificial neural network (ANN) models for determining hydraulic conductivity of compacted fine-grained soils

2009 ◽  
Vol 46 (8) ◽  
pp. 955-968 ◽  
Author(s):  
Yusuf Erzin ◽  
S. D. Gumaste ◽  
A. K. Gupta ◽  
D. N. Singh
Keyword(s):  
Hydraulic Conductivity ◽  
Absolute Error ◽  
Silty Sand ◽  
Coefficient Of Determination ◽  
Performance Indices ◽  
Fine Grained ◽  
Centrifuge Tests ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

This study deals with development of artificial neural networks (ANNs) and multiple regression analysis (MRA) models for determining hydraulic conductivity of fine-grained soils. To achieve this, conventional falling-head tests, oedometer falling-head tests, and centrifuge tests were conducted on silty sand and marine clays compacted at different dry densities and moisture contents. Further, results obtained from ANN and MRA models were compared vis-à-vis experimental results. The performance indices such as the coefficient of determination, root mean square error, mean absolute error, and variance were used to assess the performance of these models. The ANN models exhibit higher prediction performance than the MRA models based on their performance indices. It has been demonstrated that the ANN models developed in the study can be employed for determining hydraulic conductivity of compacted fine-grained soils quite efficiently.

scholarly journals Modelling and Forecasting Inbound Tourism Demand to Croatia using Artificial Neural Networks: A Comparative Study

2020 ◽  
Vol 11 (21) ◽  
pp. 55-70
Author(s):  
Murat Cuhadar
Keyword(s):  
Demand Forecasting ◽  
Tourism Industry ◽  
Percentage Error ◽  
Accurate Estimation ◽  
Tourism Demand ◽  
Tourist Flows ◽  
Ann Models ◽  
Optimal Forecasting ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Tourism demand is the basis on which all commercial decisions concerning tourism ultimately depend. Accurate estimation of tourism demand is essential for the tourism industry because it can help reduce risk and uncertainty as well as effectively provide basic information for better tourism planning. The purpose of this study is to develop the optimal forecasting model that yields the highest accuracy when compared to the forecast performances of three different methods, namely Artificial Neural Network (ANN), Exponential Smoothing, and Box-Jenkins methods for forecasting monthly inbound tourist flows to Croatia. Prior studies have been applied to forecast tourism demand to Croatia based on time series models and casual methods. However, the monthly and comparative tourism demand forecasting studies using ANNs are still limited, and this paper aims to fill this gap. The number of monthly foreign tourist arrivals to Croatia covers the period between January 2005-December 2019 data were used to build optimal forecasting models. Forecasting performances of the models were measured by Mean Absolute Percentage Error (MAPE) statistics. As a result of the experiments carried out, when compared to the forecasting performances of various models, 12 lagged ANN models, which have [4-3-1] architecture, were seen to perform best among all models applied in this study. Considering both the empirical findings obtained from this study and previous studies on tourism forecasting, it can be seen that ANN models that do not have any negativities (such as over-training, faulty architecture, etc.) produce successful forecasting results when compared with results generated by conventional statistical methods.

Estimating bearing capacity of shallow foundations by artificial neural networks

2017 ◽  
Vol 3 (4) ◽  
pp. 151
Author(s):  
Mustafa Aytekin
Keyword(s):  
Bearing Capacity ◽  
Ultimate Bearing Capacity ◽  
Training Data ◽  
Coefficient Of Determination ◽  
Average Error ◽  
Large Set ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Capacity Data

In this study, the Artificial Neural Network, ANN is applied to data extracted from a large set of random data created by using Terzaghi and Meyerhof formulae. By using MS Excel, 3750 sets of data for Terzaghi's equation, 4000 for Meyerhof's equation were generated. A simulated ANN was trained on a subset of bearing capacity data, and the performance was tested on the remaining data. The performances of the ANN models were compared to Terzaghi and Meyerhof results. ANN models were as accurate as the other techniques in estimating the ultimate bearing capacity. The models estimated the ultimate bearing capacity with an average error of around 1% of the value obtained from Terzaghi and Meyerhof equations, and the coefficient of determination (r2) was almost equal to 1. Their sensitivity and specificity is dependent on the function and the algorithm used in the training process. Validation subset is crucial in preventing the over-fitting of the ANN models to the training data. ANN models are potentially useful technique for estimating the bearing capacity of the soil. Large training data sets are needed to improve the performance of data-derived algorithms, in particular ANN models.

Monitoring-System Development of Gillnet Using Artificial Neural Network

2020 ◽  
Author(s):  
Chungkuk Jin ◽  
HanSung Kim ◽  
JeongYong Park ◽  
MooHyun Kim ◽  
Kiseon Kim
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Big Data ◽  
Learning Algorithm ◽  
System Development ◽  
Ann Model ◽  
Ann Models ◽  
Artificial Neural ◽  
Input Variables ◽  
Artificial Neural Network Ann

Abstract This paper presents a method for detecting damage to a gillnet based on sensor fusion and the Artificial Neural Network (ANN) model. Time-domain numerical simulations of a slender gillnet were performed under various wave conditions and failure and non-failure scenarios to collect big data used in the ANN model. In training, based on the results of global performance analyses, sea states, accelerations of the net assembly, and displacements of the location buoy were selected as the input variables. The backpropagation learning algorithm was employed in training to maximize damage-detection performance. The output of the ANN model was the identification of the particular location of the damaged net. In testing, big data, which were not used in training, were utilized. Well-trained ANN models detected damage to the net even at sea states that were not included in training with high accuracy.

Probing Norwalk-like virus presence in shellfish, using artificial neural networks

2004 ◽  
Vol 50 (1) ◽  
pp. 125-129 ◽  
Author(s):  
G. Brion ◽  
S. Lingeriddy ◽  
T.R. Neelakantan ◽  
M. Wang ◽  
R. Girones ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Models ◽  
Classification Performance ◽  
Relative Importance ◽  
Logistic Regression Models ◽  
Ann Models ◽  
Artificial Neural ◽  
Presence And Absence ◽  
Input Variables ◽  
Artificial Neural Network Ann

A database was examined using artificial neural network (ANN) models to investigate the efficacy of predicting PCR-identified Norwalk-like virus presence and absence in shellfish. The relative importance of variables in the model and the predictive power obtained by application of ANN modelling methods were compared with previously developed logistic regression models. In addition, two country-specific datasets were analysed separately with ANN models to determine if the relative importance of the input variables was similar for geographically diverse regions. The results of this analysis found that ANN models predicted Norwalk-like virus presence and absence in shellfish with equivalent, and better, precision than logistic regression models. For overall classification performance, ANN modelling had a rate of 93%, vs 75% for the logistic regression. ANN models were able to illuminate the site-specific relationships between indicators and pathogens.

scholarly journals ANFIS-based approach to scour depth prediction at abutments in armored beds

2010 ◽  
Vol 13 (4) ◽  
pp. 699-713 ◽  
Author(s):  
Mohammad Muzzammil ◽  
Javed Alam
Keyword(s):  
Fuzzy Inference ◽  
Accurate Estimation ◽  
Scour Depth ◽  
Bridge Abutments ◽  
Anfis Model ◽  
Inference System ◽  
Depth Prediction ◽  
Neuro Fuzzy ◽  
Artificial Neural Network Ann ◽  
The Comparative Study

An accurate estimation of the maximum possible scour depth at bridge abutments is of paramount importance in decision-making for the safe abutment foundation depth and also for the degree of scour countermeasures to be implemented against excessive scouring. Most of the scour depth prediction formulae available in the literature have been developed based on the analysis of laboratory and field data using statistical methods such as the regression method (RM). The alternative approaches, such as artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS), are generally preferred to provide better solutions in cases where the available data is incomplete or ambiguous in nature. In the present study, an attempt has, therefore, been made to develop the ANFIS model for the prediction of scour depth at the bridge abutments embedded in an armored bed and make the comparative study for the performance of ANFIS over RM and ANN in modeling the scour depth. It has been found that the ANFIS model performed best amongst all of these methods. The causative variables in raw form result in a more accurate prediction of the scour depth than that of their grouped form.

Models of Artificial Neural Networks Applied to Demand Forecasting in Nonconsolidated Tourist Destinations

Methodology ◽  
2015 ◽  
Vol 11 (2) ◽  
pp. 35-44 ◽  
Author(s):  
Tomás Molinet Berenguer ◽  
José Antonio Molinet Berenguer ◽  
María Elena Betancourt García ◽  
Alfonso Palmer Pol ◽  
Juan José Montaño Moreno
Keyword(s):  
Moving Average ◽  
Demand Forecasting ◽  
Ann Model ◽  
Tourist Destinations ◽  
Different Seasons ◽  
Auto Regressive ◽  
Ann Models ◽  
Artificial Neural ◽  
Input Variables ◽  
Artificial Neural Network Ann

This article focuses on a new proposed artificial neural network (ANN) model for tourism demand forecasting using time-series which, unlike previous models, uses different seasons of arrivals and values of months with similar behavior as input variables and achieves a forecast up to a year in advance. We demonstrate the validity and greater precision of the proposed model in forecasting a nonconsolidated destination with marked seasonality, which has been scarcely dealt with in other research. We achieve a comparatively greater quality of results and a longer period in advance than previously used auto-regressive integrated moving average (ARIMA) and ANN models. Highly accurate results were also obtained in destinations such as Portugal, which also proves its validity for mature destinations.

scholarly journals Estimating Inside Air Temperature of a Glasshouse Using Statistical Models

2017 ◽  
Vol 12 (1) ◽  
pp. 01-05 ◽  
Author(s):  
Aristidis Matsoukis ◽  
Konstantinos Chronopoulos
Keyword(s):  
Air Temperature ◽  
Mean Absolute Error ◽  
Predictor Variable ◽  
Absolute Error ◽  
Meteorological Station ◽  
Coefficient Of Determination ◽  
Ann Model ◽  
Ann Models ◽  
Artificial Neural Network Ann ◽  
Better Than

The efficiency of applying linear regression (LR) and artificial neural network (ANN) models to estimate inside air temperature (T) of a glasshouse (37o48΄20΄΄N, 23o57΄48΄΄E), Lavreotiki, was investigated in the present work. The T data from an urban meteorological station (MS) at 37058΄55΄΄N, 23o32΄14΄΄E, Athens, Attica, Greece, about 30 Km away from the glasshouse, were used as predictor variable, taking into account the actual time of measurement (ATM) and two hours earlier (ATM-2), depending on the case. Air temperature data were monitored in each examined area (glasshouse and MS) for four successive months (July-October) and averages on a two-hour basis were used for the aforementioned estimation. Results showed that ANN were better than LR models, considering their better performance as shown in the scatterplots of the distribution of observed versus estimated inside T data of the glasshouse, in terms of both higher coefficient of determination (R2) and lower mean absolute error (MAE). The best ANN model (highest R2 and lowest MAE) was achieved by using as predictor variables the T at ATM and the T at ATM-2 from MS. The findings of our study may be a first step towards the estimation of inside T of a glasshouse in Greece, from outside T data of a remote MS. Thus, the operation of the glasshouse could be improved noticeably.

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