scholarly journals Characterizing Groundwater Potential Using GIS-Based Machine Learning Model in Chihe River Basin, China

2021 ◽  
Author(s):  
Dejian Wang ◽  
Jiazhong Qian ◽  
Lei Ma ◽  
Weidong Zhao ◽  
Di Gao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Water Resources ◽  
River Basin ◽  
Environmental Variables ◽  
Groundwater Potential ◽  
High Potential ◽  
Slope Aspect ◽  
Learning Models ◽  
Topographic Wetness Index ◽  
Machine Learning Models

Abstract Mapping of groundwater potential over space, built by synergizing environmental variables and machine learning models, was of great significance for regional water resources management. Taking the Chihe River basin in Anhui province as an example, thirteen influence factors were used to predict the spatial distribution of groundwater, including elevation, slope, aspect, plan curvature, profile curvature, topographic wetness index (TWI), drainage density, distance to rivers, distance to faults, lithology, soil type, land use, and normalized difference vegetation index (NDVI). The potential of groundwater resource in this region was predicted using GIS-based machine learning models, including logistic regression (LR), deep neural networks (DNN), and random forest (RF) model. Then, the accuracy of prediction results was evaluated by calculating the RMSE, MAE and R evaluation index. The results show that there is no collinearity among the 13 environmental impact factors, which can provide corresponding environmental variables for the evaluation of regional groundwater potential. Machine learning models show that groundwater potential is concentrated in moderate to high potential areas. Among them, the moderate to the high potential of this area accounted for 81.14% in the LR model, 90.36% and 87.55% in the DNN model and the RF model, respectively. According to the result of these evaluation indexes, the three models all have high prediction accuracy, among which the LR model performs more prominently. The good prediction capabilities of these machine learning technologies can provide a reliable scientific basis for spatial prediction of groundwater potential and management of water resources.

Ensemble Boosting and Bagging Based Machine Learning Models for Groundwater Potential Prediction

2020 ◽  
Author(s):  
Amirhosein Mosavi ◽  
Farzaneh Sajedi Hosseini ◽  
Bahram Choubin ◽  
Massoud Goodarzi ◽  
Adrienn A. Dineva ◽  
...  
Keyword(s):  
Machine Learning ◽  
Groundwater Potential ◽  
Learning Models ◽  
Machine Learning Models

scholarly journals A New Integrated Approach for Landslide Data Balancing and Spatial Prediction Based on Generative Adversarial Networks (GAN)

2021 ◽  
Vol 13 (19) ◽  
pp. 4011
Author(s):  
Husam A. H. Al-Najjar ◽  
Biswajeet Pradhan ◽  
Raju Sarkar ◽  
Ghassan Beydoun ◽  
Abdullah Alamri
Keyword(s):  
Machine Learning ◽  
Spatial Prediction ◽  
Generative Models ◽  
Generative Adversarial Networks ◽  
Slope Aspect ◽  
Support Vector ◽  
Learning Models ◽  
Adversarial Networks ◽  
Landslide Data ◽  
Machine Learning Models

Landslide susceptibility mapping has significantly progressed with improvements in machine learning techniques. However, the inventory / data imbalance (DI) problem remains one of the challenges in this domain. This problem exists as a good quality landslide inventory map, including a complete record of historical data, is difficult or expensive to collect. As such, this can considerably affect one’s ability to obtain a sufficient inventory or representative samples. This research developed a new approach based on generative adversarial networks (GAN) to correct imbalanced landslide datasets. The proposed method was tested at Chukha Dzongkhag, Bhutan, one of the most frequent landslide prone areas in the Himalayan region. The proposed approach was then compared with the standard methods such as the synthetic minority oversampling technique (SMOTE), dense imbalanced sampling, and sparse sampling (i.e., producing non-landslide samples as many as landslide samples). The comparisons were based on five machine learning models, including artificial neural networks (ANN), random forests (RF), decision trees (DT), k-nearest neighbours (kNN), and the support vector machine (SVM). The model evaluation was carried out based on overall accuracy (OA), Kappa Index, F1-score, and area under receiver operating characteristic curves (AUROC). The spatial database was established with a total of 269 landslides and 10 conditioning factors, including altitude, slope, aspect, total curvature, slope length, lithology, distance from the road, distance from the stream, topographic wetness index (TWI), and sediment transport index (STI). The findings of this study have shown that both GAN and SMOTE data balancing approaches have helped to improve the accuracy of machine learning models. According to AUROC, the GAN method was able to boost the models by reaching the maximum accuracy of ANN (0.918), RF (0.933), DT (0.927), kNN (0.878), and SVM (0.907) when default parameters used. With the optimum parameters, all models performed best with GAN at their highest accuracy of ANN (0.927), RF (0.943), DT (0.923) and kNN (0.889), except SVM obtained the highest accuracy of (0.906) with SMOTE. Our finding suggests that RF balanced with GAN can provide the most reasonable criterion for landslide prediction. This research indicates that landslide data balancing may substantially affect the predictive capabilities of machine learning models. Therefore, the issue of DI in the spatial prediction of landslides should not be ignored. Future studies could explore other generative models for landslide data balancing. By using state-of-the-art GAN, the proposed model can be considered in the areas where the data are limited or imbalanced.

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

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.

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