Random Forest (RF) and Artificial Neural Network (ANN) Algorithms for LULC Mapping

2020 ◽  
Vol 38 (4A) ◽  
pp. 510-514
Author(s):  
Tay H. Shihab ◽  
Amjed N. Al-Hameedawi ◽  
Ammar M. Hamza
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Artificial Neural Network ◽  
Random Forest ◽  
Satellite Image ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Data Set ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this paper to make use of complementary potential in the mapping of LULC spatial data is acquired from LandSat 8 OLI sensor images are taken in 2019.  They have been rectified, enhanced and then classified according to Random forest (RF) and artificial neural network (ANN) methods. Optical remote sensing images have been used to get information on the status of LULC classification, and extraction details. The classification of both satellite image types is used to extract features and to analyse LULC of the study area. The results of the classification showed that the artificial neural network method outperforms the random forest method. The required image processing has been made for Optical Remote Sensing Data to be used in LULC mapping, include the geometric correction, Image Enhancements, The overall accuracy when using the ANN methods 0.91 and the kappa accuracy was found 0.89 for the training data set. While the overall accuracy and the kappa accuracy of the test dataset were found 0.89 and 0.87 respectively.

A Model on the Correlation between Composition and Mechanical Properties of Mg-Al-Zn Alloys by Using Artificial Neural Network

2005 ◽  
Vol 488-489 ◽  
pp. 793-796 ◽  
Author(s):  
Hai Ding Liu ◽  
Ai Tao Tang ◽  
Fu Sheng Pan ◽  
Ru Lin Zuo ◽  
Ling Yun Wang
Keyword(s):  
Neural Network ◽  
Mechanical Properties ◽  
Artificial Neural Network ◽  
Magnesium Alloys ◽  
Tensile Yield Strength ◽  
Data Set ◽  
The Neural Network ◽  
Artificial Neural ◽  
Prediction Of Mechanical Properties ◽  
Artificial Neural Network Ann

A model was developed for the analysis and prediction of correlation between composition and mechanical properties of Mg-Al-Zn (AZ) magnesium alloys by applying artificial neural network (ANN). The input parameters of the neural network (NN) are alloy composition. The outputs of the NN model are important mechanical properties, including ultimate tensile strength, tensile yield strength and elongation. The model is based on multilayer feedforward neural network. The NN was trained with comprehensive data set collected from domestic and foreign literature. A very good performance of the neural network was achieved. The model can be used for the simulation and prediction of mechanical properties of AZ system magnesium alloys as functions of composition.

scholarly journals Modeling of daily runoff from a small agricultural watershed using artificial neural network with resampling techniques

2014 ◽  
Vol 17 (1) ◽  
pp. 56-74 ◽  
Author(s):  
Gurjeet Singh ◽  
Rabindra K. Panda ◽  
Marc Lamers
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Agricultural Watershed ◽  
Training Data ◽  
Optimum Number ◽  
Training Process ◽  
Data Set ◽  
Reported Study ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The reported study was undertaken in a small agricultural watershed, namely, Kapgari in Eastern India having a drainage area of 973 ha. The watershed was subdivided into three sub-watersheds on the basis of drainage network and land topography. An attempt was made to relate the continuously monitored runoff data from the sub-watersheds and the whole-watershed with the rainfall and temperature data using the artificial neural network (ANN) technique. The reported study also evaluated the bias in the prediction of daily runoff with shorter length of training data set using different resampling techniques with the ANN modeling. A 10-fold cross-validation (CV) technique was used to find the optimum number of hidden neurons in the hidden layer and to avoid neural network over-fitting during the training process for shorter length of data. The results illustrated that the ANN models developed with shorter length of training data set avoid neural network over-fitting during the training process, using a 10-fold CV method. Moreover, the biasness was investigated using the bootstrap resampling technique based ANN (BANN) for short length of training data set. In comparison with the 10-fold CV technique, the BANN is more efficient in solving the problems of the over-fitting and under-fitting during training of models for shorter length of data set.

scholarly journals Artificial neural network classification-based skin cancer detection

2017 ◽  
Vol 7 (1.1) ◽  
pp. 591
Author(s):  
M. Shyamala Devi ◽  
A.N. Sruthi ◽  
P. Balamurugan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Skin Cancer ◽  
Back Propagation ◽  
Modern Technology ◽  
Data Set ◽  
Processing Strategies ◽  
Skin Cancers ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

At present, skin cancers are extremely the most severe and life-threatening kind of cancer. The majority of the pores and skin cancers are completely remediable at premature periods. Therefore, a premature recognition of pores and skin cancer can effectively protect the patients. Due to the progress of modern technology, premature recognition is very easy to identify. It is not extremely complicated to discover the affected pores and skin cancers with the exploitation of Artificial Neural Network (ANN). The treatment procedure exploits image processing strategies and Artificial Intelligence. It must be noted that, the dermoscopy photograph of pores and skin cancer is effectively determined and it is processed to several pre-processing for the purpose of noise eradication and enrichment in image quality. Subsequently, the photograph is distributed through image segmentation by means of thresholding. Few components distinctive for skin most cancers regions. These features are mined the practice of function extraction scheme - 2D Wavelet Transform scheme. These outcomes are provides to the Back-Propagation Neural (BPN) Network for effective classification. This completely categorizes the data set into either cancerous or non-cancerous. 

scholarly journals Comparison of Accuracy of Artificial Neural Network (ANN) and Kriging Methods for Estimating Chlorine Concentration

10.29007/lpmh ◽  
2018 ◽  
Author(s):  
Faezeh Ghaleh Navi ◽  
Hamed Mazandarani Zadeh ◽  
Dragan Savic
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Kriging Model ◽  
Coefficient Of Determination ◽  
Monthly Basis ◽  
Data Set ◽  
The Neural Network ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Observation Wells

Groundwater is one of the major sources of fresh water. Maintenance and management of this vital resource is so important especially in arid and semi-arid regions. Reliable and accurate groundwater quality assessment is essential as a basic data for any groundwater management studies. The aim of this study is to compare the accuracy of two Artificial Neural Network (ANN) and Kriging methods in predicting chlorine in groundwater. In case of ANN, we created an appropriate emulator, which minimize the prediction error by changing the parameters of the neural network, including the number of layers. The best Kriging model is also obtained by changing the variogram function, such that the Gaussian variogram has the least error in interpolation of the amount of chlorine. To evaluate the accuracy of these two methods, the mean square error (MSE) and Coefficient of determination (R2) are used. The data set consists of the amount of chlorine, in a monthly basis, measured at 112 observation wells from 1999 to 2015 in aquifer Qazvin, Iran. MSE values for ANN and Kriging are 14.8 and 15.4, respectively, which indicate that the ANN has a better performance and is more capable of predicting chlorine values in comparison with Kriging.

scholarly journals Utilizing Soft Computing for Determining Protein Deficiency

2013 ◽  
Vol 5 (1) ◽  
Author(s):  
Sri Hartati ◽  
Sri Nurdiati
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Protein Deficiency ◽  
Growth Delay ◽  
Backpropagation Neural Network ◽  
Data Set ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Result Analysis ◽  
Early Diagnose

Abstract— In recent years, the occurrence of protein shortage of children under 5 years old in many poor area has dramatically increased. Since this situation can cause serious problem to children like a delay in their growth, delay in their development and also disfigurement, disability, dependency, the early diagnose of protein shortage is vital. Many applications have been developed in performing disease detection such as an expert system for diagnosing diabetics and artificial neural network (ANN) applications for diagnosing breast cancer, acidosis diseases, and lung cancer. This paper is mainly focusing on the development of protein shortage disease diagnosing application using Backpropagation Neural Network (BPNN) technique. It covers two classes of protein shortage that are Heavy Protein Deficiency. On top of this, a BPNN model is constructed based on result analysis of the training and testing from the developed application. The model has been successfully tested using new data set. It shows that the BPNN is able to early diagnose heavy protein deficiency accurately. Keywords— Artificial Neural Network, Backpropagation Neural Network, Protein Deficiency.

scholarly journals A Comparison of Emotional Neural Network (ENN) and Artificial Neural Network (ANN) Approach for Rainfall-Runoff Modelling

2019 ◽  
Vol 5 (10) ◽  
pp. 2120-2130 ◽  
Author(s):  
Suraj Kumar ◽  
Thendiyath Roshni ◽  
Dar Himayoun
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Data Sets ◽  
Rainfall Runoff ◽  
Data Set ◽  
Discharge Data ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Emotional Neural Network ◽  
Selection Of

Reliable method of rainfall-runoff modeling is a prerequisite for proper management and mitigation of extreme events such as floods. The objective of this paper is to contrasts the hydrological execution of Emotional Neural Network (ENN) and Artificial Neural Network (ANN) for modelling rainfall-runoff in the Sone Command, Bihar as this area experiences flood due to heavy rainfall. ENN is a modified version of ANN as it includes neural parameters which enhance the network learning process. Selection of inputs is a crucial task for rainfall-runoff model. This paper utilizes cross correlation analysis for the selection of potential predictors. Three sets of input data: Set 1, Set 2 and Set 3 have been prepared using weather and discharge data of 2 raingauge stations and 1 discharge station located in the command for the period 1986-2014.  Principal Component Analysis (PCA) has then been performed on the selected data sets for selection of data sets showing principal tendencies.  The data sets obtained after PCA have then been used in the model development of ENN and ANN models. Performance indices were performed for the developed model for three data sets. The results obtained from Set 2 showed that ENN with R= 0.933, R2 = 0.870, Nash Sutcliffe = 0.8689, RMSE = 276.1359 and Relative Peak Error = 0.00879 outperforms ANN in simulating the discharge. Therefore, ENN model is suggested as a better model for rainfall-runoff discharge in the Sone command, Bihar.

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