scholarly journals A Comparison Between Major Artificial Intelligence Models for Crop Yield Prediction: Case Study of the Midwestern United States, 2006–2015

2019 ◽  
Vol 8 (5) ◽  
pp. 240 ◽  
Author(s):  
Nari Kim ◽  
Kyung-Ja Ha ◽  
No-Wook Park ◽  
Jaeil Cho ◽  
Sungwook Hong ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
United States ◽  
Crop Yield ◽  
Layer Structure ◽  
Absolute Error ◽  
Activation Function ◽  
Drop Out ◽  
Yield Prediction ◽  
Midwestern United States ◽  
Artificial Intelligence Models

This paper compares different artificial intelligence (AI) models in order to develop the best crop yield prediction model for the Midwestern United States (US). Through experiments to examine the effects of phenology using three different periods, we selected the July–August (JA) database as the best months to predict corn and soybean yields. Six different AI models for crop yield prediction are tested in this research. Then, a comprehensive and objective comparison is conducted between the AI models. Particularly for the deep neural network (DNN) model, we performed an optimization process to ensure the best configurations for the layer structure, cost function, optimizer, activation function, and drop-out ratio. In terms of mean absolute error (MAE), our DNN model with the JA database was approximately 21–33% and 17–22% more accurate for corn and soybean yields, respectively, than the other five AI models. This indicates that corn and soybean yields for a given year can be forecasted in advance, at the beginning of September, approximately a month or more ahead of harvesting time. A combination of the optimized DNN model and spatial statistical methods should be investigated in future work, to mitigate partly clustered errors in some regions.

scholarly journals Crop Yield Prediction using XG Boost Algorithm

2020 ◽  
Vol 8 (5) ◽  
pp. 3516-3520
Keyword(s):  
Crop Yield ◽  
Tamil Nadu ◽  
Crop Yields ◽  
Absolute Error ◽  
Climatic Conditions ◽  
Support Vector ◽  
Yield Prediction ◽  
Indian States ◽  
South Indian ◽  
Proposed Model

The main objective of this research is to predict crop yields based on cultivation area, Rainfall and maximum and minimum temperature data. It will help our Indian farmers to predict crop yielding according to the environment conditions. Nowadays, Machine learning based crop yield prediction is very popular than the traditional models because of its accuracy. In this paper, linear regression, Support Vector Regression, Decision Tree and Random forest is compared with XG Boost algorithm. The above mentioned algorithms are compared based on R2 , Minimum Square Error and Minimum Absolute Error. The dataset is prepared from the data.gov.in site for the year from 2000 to 2014. The data for 4 south Indian states Andhra Pradesh, Karnataka, Tamil Nadu and Kerala data alone is taken since all these states has same climatic conditions. The proposed model in this paper based on XG Boost is showing much better results than other models. In XG Boost R2 is 0.9391 which is the best when compared with other models.

scholarly journals Application of Artificial Intelligence Techniques to Predict the Well Productivity of Fishbone Wells

2019 ◽  
Vol 11 (21) ◽  
pp. 6083 ◽  
Author(s):  
Amjed Hassan ◽  
Salaheldin Elkatatny ◽  
Abdulazeez Abdulraheem
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Artificial Neural Network ◽  
Absolute Error ◽  
Well Performance ◽  
Well Productivity ◽  
Artificial Intelligence Models ◽  
Actual Flow ◽  
Artificial Neural ◽  
Effective Models

Fishbone multilateral wells are applied to enhance well productivity by increasing the contact area between the bottomhole and reservoir region. Fishbone wells are characterized by reduced operational time and a competitive cost in comparison to hydraulic fracturing operations. However, limited models are reported to determine the productivity of fishbone wells. In this paper, several artificial intelligence methods were applied to estimate the performance of fishbone wells producing from a heterogeneous and anisotropic gas reservoir. The well productivity was determined using an artificial neural network, a fuzzy logic system and a radial basis network. The models were developed and validated utilizing 250 data sets, with the inputs being the permeability ratio (Kh/Kv), flowing bottomhole pressure and lateral length. The results showed that the artificial intelligence models were able to predict the fishbone well productivity with an acceptable absolute error of 7.23%. Moreover, a mathematical equation was extracted from the artificial neural network, which is able to provide a simple and direct estimation of fishbone well productivity. Actual flow tests were used to evaluate the reliability of the developed model, and a very acceptable match was obtained between the predicted and actual flow rates, wherein an absolute error of 6.92% was achieved. This paper presents effective models for determining the well performance of complex multilateral wells producing from heterogeneous reservoirs. The developed models will help to reduce the uncertainty associated with numerical methods, and the extracted equation can be inserted into commercial software, thereby significantly reducing deviation between the actual data and simulated results.

scholarly journals Satellite detection of cover crops and their effects on crop yield in the Midwestern United States

2018 ◽  
Vol 13 (6) ◽  
pp. 064033 ◽  
Author(s):  
Christopher A Seifert ◽  
George Azzari ◽  
David B Lobell
Keyword(s):  
United States ◽  
Crop Yield ◽  
Cover Crops ◽  
Midwestern United States

scholarly journals A comparison of activation functions in multilayer neural network for predicting the production and consumption of electricity power

2021 ◽  
Vol 11 (1) ◽  
pp. 163
Author(s):  
Abdulwahed Salam ◽  
Abdelaaziz El Hibaoui ◽  
Abdulgabbar Saif
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Activation Function ◽  
Activation Functions ◽  
Paper Cover ◽  
Intelligence Models ◽  
Artificial Intelligence Models ◽  
Electricity Power ◽  
Multilayer Neural Networks ◽  
Power Utilities

Predicting electricity power is an important task, which helps power utilities in improving their systems’ performance in terms of effectiveness, productivity, management and control. Several researches had introduced this task using three main models: engineering, statistical and artificial intelligence. Based on the experiments, which used artificial intelligence models, multilayer neural networks model has proven its success in predicting many evaluation datasets. However, the performance of this model depends mainly on the type of activation function. Therefore, this paper introduces an experimental study for investigating the performance of the multilayer neural networks model with respect to different activation functions and different depths of hidden layers. The experiments in this paper cover the comparison among eleven activation functions using four benchmark electricity datasets. The activation functions under examination are sigmoid, hyperbolic tangent, SoftSign, SoftPlus, ReLU, Leak ReLU, Gaussian, ELU, SELU, Swish and Adjust-Swish. Experimental results show that ReLU and Leak ReLU activation functions outperform their counterparts in all datasets.

Inequality in the Promised Land

2020 ◽  
Vol 9 (1) ◽  
pp. 177-180
Author(s):  
Muhammad Sharif Uddin
Keyword(s):  
United States ◽  
Minority Students ◽  
Doctoral Dissertation ◽  
Ethnographic Study ◽  
Suburban Area ◽  
Midwestern United States ◽  
Minority Community ◽  
Promised Land ◽  
Land Race ◽  
The Relationship

Inequality in the promised land: Race, resources, and suburban schooling is a well-written book by L’ Heureux Lewis-McCoy. The book is based on Lewis-McCoy’s doctoral dissertation, that included an ethnographic study in a suburban area named Rolling Acres in the Midwestern United States. Lewis-McCoy studied the relationship between families and those families’ relationships with schools. Through this study, the author explored how invisible inequality and racism in an affluent suburban area became the barrier for racial and economically minority students to grow up academically. Lewis-McCoy also discovered the hope of the minority community for raising their children for a better future.

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