.png){kind=icon}
返回
Ensemble Learning Paradigms for Flow Rate Prediction Boosting
.png){kind=icon}
OA
AI
DOI：10.1007/s11269-023-03562-5.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In response to the issue of water scarcity in recent years, international organizations, in collaboration with many governments, have initiated several drinking water supply projects carried out by geophysical and drilling companies. Unfortunately, despite the reliability of electrical resistivity profiling (ERP) and vertical electrical sounding (VES) methods, the substantial financial losses incurred due to numerous unsuccessful drillings are owing to the difficulty to emphasize the drilling location properly. Therefore, we proposed the ensemble machine learning (EML) paradigms to predict the flow rate (FR) with an optimal score before any drilling operations. The approach was experimented in a region with severe water shortages. Thus, geo-electrical features from the ERP and VES were defined and coupled with borehole data to create the binary dataset (FR <= 1m(3)/hr and FR > 1m(3)/hr for unproductive and productive boreholes respectively). Then, the dataset is state-of-art transformed before feeding to the EML algorithms. The model performance and generalization capability were evaluated using the Matthews correlation, the accuracy, the confusion matrix, the binary predictor error, the precision-recall, and the cumulative gain plot. As a result, the benchmark, pasting, extreme gradient boosting, and stacking paradigms have built a powerful range of FR prediction scores between 90 similar to 96%. Henceforth, the robust EML paradigms can be used to identify the best location for drilling operations, lowering the repercussion of unsuccessful drillings.
Keyword:
Ensemble machine learning
Electrical method
Flow rate prediction
Water
期刊
IF:
4.7
论文数:
8.1K
被引数:
1.6W
机构
引用论文
Comparative Study of Different Discrete Wavelet Based Neural Network Models for long term Drought Forecasting不同离散小波神经网络模型在长期干旱预测中的比较研究
PREAI
分享收藏Assessment of Climate Change Impact on Water Balance of Lake Hawassa Catchment气候变化对哈沙萨湖流域水平衡影响的评估
PREAI
分享收藏Bayesian machine learning ensemble approach to quantify model uncertainty in predicting groundwater storage change贝叶斯机器学习集成方法，用于量化预测地下水储量变化的模型不确定性
PREAI
分享收藏Geo-resistivity data set for groundwater aquifer exploration in the basement complex terrain of Nigeria, West Africa用于在西非尼日利亚的地下复杂地形中进行地下水含水层勘探的地质电阻率数据集.png)
DATA IN BRIEF
OAAI
DATA IN BRIEFIF1.4
OAAI分享收藏
查看更多内容