scholarly journals Spatial Prediction of Groundwater Potentiality in Large Semi-Arid and Karstic Mountainous Region Using Machine Learning Models

Water ◽  
2021 ◽  
Vol 13 (16) ◽  
pp. 2273
Author(s):  
Mustapha Namous ◽  
Mohammed Hssaisoune ◽  
Biswajeet Pradhan ◽  
Chang-Wook Lee ◽  
Abdullah Alamri ◽  
...  
Keyword(s):  
Machine Learning ◽  
Water Scarcity ◽  
Rural Areas ◽  
Information Gain ◽  
Arid Zones ◽  
Learning Models ◽  
Statistical Measures ◽  
Groundwater Potentiality ◽  
Semi Arid ◽  
Machine Learning Models

The drinking and irrigation water scarcity is a major global issue, particularly in arid and semi-arid zones. In rural areas, groundwater could be used as an alternative and additional water supply source in order to reduce human suffering in terms of water scarcity. In this context, the purpose of the present study is to facilitate groundwater potentiality mapping via spatial-modelling techniques, individual and ensemble machine-learning models. Random forest (RF), logistic regression (LR), decision tree (DT) and artificial neural networks (ANNs) are the main algorithms used in this study. The preparation of groundwater potentiality maps was assembled into 11 ensembles of models. Overall, about 374 groundwater springs was identified and inventoried in the mountain area. The spring inventory data was randomly divided into training (75%) and testing (25%) datasets. Twenty-four groundwater influencing factors (GIFs) were selected based on a multicollinearity test and the information gain calculation. The results of the groundwater potentiality mapping were validated using statistical measures and the receiver operating characteristic curve (ROC) method. Finally, a ranking of the 15 models was achieved with the prioritization rank method using the compound factor (CF) method. The ensembles of models are the most stable and suitable for groundwater potentiality mapping in mountainous aquifers compared to individual models based on success and prediction rate. The most efficient model using the area under the curve validation method is the RF-LR-DT-ANN ensemble of models. Moreover, the results of the prioritization rank indicate that the best models are the RF-DT and RF-LR-DT ensembles of models.

scholarly journals Predicting Energy Demand in Semi-Remote Arctic Locations

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 798
Author(s):  
Odin Foldvik Eikeland ◽  
Filippo Maria Bianchi ◽  
Harry Apostoleris ◽  
Morten Hansen ◽  
Yu-Cheng Chiou ◽  
...  
Keyword(s):  
Machine Learning ◽  
Rural Areas ◽  
Energy Demand ◽  
Alternative Energy ◽  
Distribution Network ◽  
Energy Resources ◽  
The Arctic ◽  
Learning Approaches ◽  
Learning Models ◽  
Machine Learning Models

Forecasting energy demand within a distribution network is essential for developing strategies to manage and optimize available energy resources and the associated infrastructure. In this study, we consider remote communities in the Arctic located at the end of the radial distribution network without alternative energy supply. Therefore, it is crucial to develop an accurate forecasting model to manage and optimize the limited energy resources available. We first compare the accuracy of several models that perform short-and medium-term load forecasts in rural areas, where a single industrial customer dominates the electricity consumption. We consider both statistical methods and machine learning models to predict energy demand. Then, we evaluate the transferability of each method to a geographical rural area different from the one considered for training. Our results indicate that statistical models achieve higher accuracy on longer forecast horizons relative to neural networks, while the machine-learning approaches perform better in predicting load at shorter time intervals. The machine learning models also exhibit good transferability, as they manage to predict well the load at new locations that were not accounted for during training. Our work will serve as a guide for selecting the appropriate prediction model and apply it to perform energy load forecasting in rural areas and in locations where historical consumption data may be limited or even not available.

scholarly journals Prediction of evaporation in arid and semi-arid regions: a comparative study using different machine learning models

2019 ◽  
Vol 14 (1) ◽  
pp. 70-89 ◽  
Author(s):  
Zaher Mundher Yaseen ◽  
Anas Mahmood Al-Juboori ◽  
Ufuk Beyaztas ◽  
Nadhir Al-Ansari ◽  
Kwok-Wing Chau ◽  
...  
Keyword(s):  
Machine Learning ◽  
Comparative Study ◽  
Arid Regions ◽  
Learning Models ◽  
Semi Arid ◽  
Machine Learning Models

Identification of the most suitable afforestation sites by Juniperus excels specie using machine learning models: Firuzkuh semi-arid region, Iran

2021 ◽  
pp. 101427
Author(s):  
Saleh Yousefi ◽  
Mohammadtaghi Avand ◽  
Peyman Yariyan ◽  
Hasan Jahanbazi Gojani ◽  
Costache Romulus ◽  
...  
Keyword(s):  
Machine Learning ◽  
Arid Region ◽  
Learning Models ◽  
Semi Arid Region ◽  
Semi Arid ◽  
Machine Learning Models

Improving XGBoost with Imagination Sampling

2020 ◽  
Vol 2 (1) ◽  
pp. 3-6
Author(s):  
Eric Holloway
Keyword(s):  
Machine Learning ◽  
General System ◽  
Learning Models ◽  
Starting Point ◽  
Machine Learning Models

Imagination Sampling is the usage of a person as an oracle for generating or improving machine learning models. Previous work demonstrated a general system for using Imagination Sampling for obtaining multibox models. Here, the possibility of importing such models as the starting point for further automatic enhancement is explored.

Epigenetic Target Prediction with Accurate Machine Learning Models

2021 ◽  
Author(s):  
Norberto Sánchez-Cruz ◽  
Jose L. Medina-Franco
Keyword(s):  
Machine Learning ◽  
Small Molecules ◽  
Predictive Models ◽  
Large Scale ◽  
Target Prediction ◽  
Quantitative Measure ◽  
Learning Models ◽  
Discovery Research ◽  
Drug Discovery Research ◽  
Machine Learning Models

<p>Epigenetic targets are a significant focus for drug discovery research, as demonstrated by the eight approved epigenetic drugs for treatment of cancer and the increasing availability of chemogenomic data related to epigenetics. This data represents a large amount of structure-activity relationships that has not been exploited thus far for the development of predictive models to support medicinal chemistry efforts. Herein, we report the first large-scale study of 26318 compounds with a quantitative measure of biological activity for 55 protein targets with epigenetic activity. Through a systematic comparison of machine learning models trained on molecular fingerprints of different design, we built predictive models with high accuracy for the epigenetic target profiling of small molecules. The models were thoroughly validated showing mean precisions up to 0.952 for the epigenetic target prediction task. Our results indicate that the herein reported models have considerable potential to identify small molecules with epigenetic activity. Therefore, our results were implemented as freely accessible and easy-to-use web application.</p>

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