Artificial neural network exploration of the influential factors in drainage network derivation

Based on predictions of the mine inflow of water and the complexity of influential factors, a method of BP neural network is put forward for mine inrush water prediction in this paper. We chose proper impact factors and establish non-linear artificial neural network prediction model after analyzed the impact factors of mine water inflow in Shandong Heiwang iron, and also made one prediction with normal mine water inflow during the iron mining operation. It turned out that the result can match with the actual prediction data, which make it possible to predict the mine water inflow with the prediction of Artificial Neural Network.

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Implementation of Backpropagation Artificial Neural Networks to Predict Palm Oil Price Fresh Fruit Bunches

IJISTECH (International Journal Of Information System & Technology) ◽

10.30645/ijistech.v2i1.17 ◽

2018 ◽

Vol 2 (1) ◽

pp. 26

Author(s):

Edi Ismanto ◽

Noverta Effendi ◽

Eka Pandu Cynthia

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Palm Oil ◽

Oil Palm ◽

Influential Factors ◽

Fresh Fruit ◽

Selling Price ◽

Architecture Model ◽

Artificial Neural ◽

Fresh Fruit Bunches

Riau Province is one of the regions known for its plantation products, especially in the oil palm sector, so that Riau Province and regional districts focus on oil palm plants as the main commodity of plantations in Riau. Based on data from the Central Bureau of Statistics (BPS) of Riau Province, the annual production of oil palm plantations, especially smallholder plantations in Riau province has always increased. So is the demand for world CPO. But sometimes the selling price of oil palm fresh fruit bunches (FFB) for smallholder plantations always changes due to many influential factors. With the Artificial Neural Network approach, the Backpropagation algorithm we conduct training and testing of the time series variables that affect the data, namely data on the area of oil palm plantations in Riau Province; Total palm oil production in Riau Province; Palm Oil Productivity in Riau Province; Palm Oil Exports in Riau Province and Average World CPO Prices. Then price predictions will be made in the future. Based on the results of the training and testing, the best Artificial Neural Network (ANN) architecture model was obtained with 9 input layers, 5 hidden layers and 1 output layer. The output of RMSE 0000699 error value and accuracy percentage is 99.97% so that it can make price predictions according to the given target value.

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Predictive Modelling for Blasting-Induced Vibrations from Open-Pit Excavations

Applied Sciences ◽

10.3390/app11167487 ◽

2021 ◽

Vol 11 (16) ◽

pp. 7487

Author(s):

Yo-Hyun Choi ◽

Sean Seungwon Lee

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Prediction Models ◽

Predictive Modelling ◽

Influential Factors ◽

Open Pit ◽

Site Factors ◽

Empirical Formulas ◽

Ground Conditions ◽

Artificial Neural

Reliable estimates of peak particle velocity (PPV) from blasting-induced vibrations at a construction site play a crucial role in minimizing damage to nearby structures and maximizing blasting efficiency. However, reliably estimating PPV can be challenging due to complex connections between PPV and influential factors such as ground conditions. While many efforts have been made to estimate PPV reliably, discrepancies remain between measured and predicted PPVs. Here, we analyzed various methods for assessing PPV with several key relevant factors and 1,191 monitored field blasting records at 50 different open-pit sites across South Korea to minimize the discrepancies. Eight prediction models are used based on artificial neural network, conventional empirical formulas, and multivariable regression analyses. Seven influential factors were selected to develop the prediction models, including three newly included and four already formulated in empirical formulas. The three newly included factors were identified to have a significant influence on PPV, as well as the four existing factors, through a sensitivity analysis. The measured and predicted PPVs were compared to evaluate the performances of prediction models. The assessment of PPVs by an artificial neural network yielded the lowest errors, and site factors, K and m were proposed for preliminary open-pit blasting designs.

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Cluster Forecasting of Corruption Using Nonlinear Autoregressive with Exogenous Variables (NARX) – An Artificial Neural Network Analysis

10.20944/preprints202108.0247.v1 ◽

2021 ◽

Author(s):

SeyedAli Ghahari ◽

Cesar Quieroz ◽

Samuel Labi ◽

Sue McNeil

Keyword(s):

Neural Network ◽

Time Series ◽

Artificial Neural Network ◽

Influential Factors ◽

Global Perspective ◽

Neural Network Modeling ◽

Monolithic Group ◽

Artificial Neural ◽

Mean Square Errors ◽

Nonlinear Autoregressive

Any effort to combat corruption can benefit from an examination of past and projected worldwide trends. In this paper, we forecast the level of corruption in countries by integrating an artificial neural network modeling and time series analysis. The data were obtained from 113 countries from 2007 to 2017. The study is carried out at two levels: (a) global level where all countries are considered as a monolithic group; and (b) cluster level, where countries are placed into groups based on their development-related attributes. For each cluster, we use the findings from our previous study on the cluster analysis of global corruption using machine learning methods that identified the four most influential corruption factors, and we use those as independent variables. Then, using the identified influential factors, we forecast the level of corruption in each cluster using a nonlinear autoregressive recurrent neural network with exogenous inputs (NARX), an artificial neural network technique. The NARX models were developed for each cluster, with the objective function in terms of the Corruption Perceptions Index (CPI). For each model, the optimal neural network is determined by finetuning the hyperparameters. The analysis was repeated for all countries as a single group. The accuracy of the models is assessed by comparing the mean square errors (MSE) of the time series models. The results suggest that the NARX artificial neural network technique yields reliable future values of CPI globally or for each cluster of countries. This can assist policymakers and organizations in assessing the expected efficacies of their current or future corruption control policies from a global perspective as well as for groups of countries.

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Adsorption of benzene on soils under different influential factors: an experimental investigation, importance order and prediction using artificial neural network

Journal of Environmental Management ◽

10.1016/j.jenvman.2022.114467 ◽

2022 ◽

Vol 306 ◽

pp. 114467

Author(s):

Qian Wang ◽

Jianmin Bian ◽

Dongmei Ruan ◽

Chunpeng Zhang

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Experimental Investigation ◽

Influential Factors ◽

Artificial Neural

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Assessment of predictive models for the estimation of heat consumption in kindergartens

Thermal Science ◽

10.2298/tsci201026084j ◽

2021 ◽

pp. 84-84

Author(s):

Nebojsa Jurisevic ◽

Dusan Gordic ◽

Arso Vukicevic

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Linear Regression ◽

Decision Tree ◽

Multiple Linear Regression ◽

Predictive Models ◽

Influential Factors ◽

Data Availability ◽

Heat Consumption ◽

Artificial Neural

The service sector remains the only economic sector that has recorded an increase (3.8%) in energy consumption during the last decade, and it is projected to grow more than 50% in the following decades. Among the public buildings, educational are especially important since they have high abundance, great retrofit potential in terms of energy savings and impact in promoting a culture of energy efficiency. Since predictive models have shown high potential in optimizing usage of energy in buildings, this study aimed to assess their application for both finding the most influential factors on heat consumption in public kindergarten and heat consumption prediction. Two linear (Simple and Multiple Linear Regression) and two non-linear (Decision Tree and Artificial Neural Network) predictive models were utilized to estimate monthly heat consumption in 11 public kindergartens in the city of Kragujevac, Serbia. Top-performing and most complex to develop was the Artificial Neural Network predictive model. Contrary to that, Simple Linear Regression was the least precise but the most simple to develop. It was found that Multiple Linear Regression and Decision Tree were relatively simple to develop and interpret, where in particular the Multiple Linear Regression provided relatively satisfying results with a good balance of precision and usability. It was concluded that the selection of proper predictive methods depends on data availability, and technical abilities of those who utilize and create them, often offering the choice between simplicity and precision.

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