A Comparative Assessment Between Three Machine Learning Models and Their Performance Comparison by Bivariate and Multivariate Statistical Methods in Groundwater Potential Mapping

2015 ◽  
Vol 29 (14) ◽  
pp. 5217-5236 ◽  
Author(s):  
Seyed Amir Naghibi ◽  
Hamid Reza Pourghasemi
Keyword(s):  
Machine Learning ◽  
Groundwater Potential ◽  
Performance Comparison ◽  
Comparative Assessment ◽  
Learning Models ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical ◽  
Potential Mapping ◽  
Groundwater Potential Mapping ◽  
Machine Learning Models

scholarly journals The effect of sample size on different machine learning models for groundwater potential mapping in mountain bedrock aquifers

CATENA ◽  
2020 ◽  
Vol 187 ◽  
pp. 104421 ◽  
Author(s):  
Davoud Davoudi Moghaddam ◽  
Omid Rahmati ◽  
Mahdi Panahi ◽  
John Tiefenbacher ◽  
Hamid Darabi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Sample Size ◽  
Groundwater Potential ◽  
Learning Models ◽  
Potential Mapping ◽  
Bedrock Aquifers ◽  
Groundwater Potential Mapping ◽  
Machine Learning Models

scholarly journals Regional Mapping of Groundwater Potential in Ar Rub Al Khali, Arabian Peninsula Using the Classification and Regression Trees Model

2021 ◽  
Vol 13 (12) ◽  
pp. 2300
Author(s):  
Samy Elmahdy ◽  
Tarig Ali ◽  
Mohamed Mohamed
Keyword(s):  
Machine Learning ◽  
Regional Scale ◽  
Regression Trees ◽  
Classification And Regression Trees ◽  
Groundwater Potential ◽  
Machine Learning Algorithms ◽  
Conditioning Factors ◽  
Potential Mapping ◽  
Classification And Regression ◽  
Groundwater Potential Mapping

Mapping of groundwater potential in remote arid and semi-arid regions underneath sand sheets over a very regional scale is a challenge and requires an accurate classifier. The Classification and Regression Trees (CART) model is a robust machine learning classifier used in groundwater potential mapping over a very regional scale. Ten essential groundwater conditioning factors (GWCFs) were constructed using remote sensing data. The spatial relationship between these conditioning factors and the observed groundwater wells locations was optimized and identified by using the chi-square method. A total of 185 groundwater well locations were randomly divided into 129 (70%) for training the model and 56 (30%) for validation. The model was applied for groundwater potential mapping by using optimal parameters values for additive trees were 186, the value for the learning rate was 0.1, and the maximum size of the tree was five. The validation result demonstrated that the area under the curve (AUC) of the CART was 0.920, which represents a predictive accuracy of 92%. The resulting map demonstrated that the depressions of Mondafan, Khujaymah and Wajid Mutaridah depression and the southern gulf salt basin (SGSB) near Saudi Arabia, Oman and the United Arab Emirates (UAE) borders reserve fresh fossil groundwater as indicated from the observed lakes and recovered paleolakes. The proposed model and the new maps are effective at enhancing the mapping of groundwater potential over a very regional scale obtained using machine learning algorithms, which are used rarely in the literature and can be applied to the Sahara and the Kalahari Desert.

scholarly journals Significance Of Multilayer Perceptron Model For Early Detection Of Diabetes Over Ml Methods

2021 ◽  
Vol 23 (08) ◽  
pp. 148-160
Author(s):  
Dr. V.Vasudha Rani ◽  
◽  
Dr. G. Vasavi ◽  
Dr. K.R.N Kiran Kumar ◽  
◽  
...  
Keyword(s):  
Machine Learning ◽  
Multilayer Perceptron ◽  
Predictive Analytics ◽  
Early Stage ◽  
Feature Selection Method ◽  
Health Condition ◽  
Performance Comparison ◽  
Learning Approaches ◽  
Learning Models ◽  
Machine Learning Models

Diabetes is one of the chronicdiseases in the world. Millions of people are suffering with several other health issues caused by diabetes, every year. Diabetes has got three stages such as type2, type1 and insulin. Curing of diabetes disease at later stages is practically difficult. Here in this paper, we proposed a DNN model and its performance comparison with some of the machine learning models to predict the disease at an earlystage based on the current health condition of the patient. An artificial neural network (ANN) is a predictive model designed to work the same way a human brain does and works better with larger datasets. Having the concept of hidden layers, neural networks work better at predictive analytics and can make predictions with more accuracy. Novelty of this work lies in integration of feature selection method used to optimize the Multilayer Perceptron (MLP) to reduce the number of required input attributes. The results achieved using this method and several conventional machines learning approaches such as Logistic Regression, Random Forest Classifier (RFC) are compared. The proposed DNN method is proved to show better accuracy than Machine learning models for early stage detection of diabetes. This paper work is applicable to clinical support as a tool for making predecisions by the doctors and physicians.

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