Enhanced Optimization-Based Voting Classifier and Chained Multi-Objective Regressor for Effective Groundwater Resource Management

• Published in: IEEE access Vol. 9; pp. 168329 - 168341
• Main Authors: Rizwan, Atif, Khan, Anam Nawaz, Iqbal, Naeem, Ahmad, Rashid, Kim, Do Hyeun
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Classifiers; Color; Drilling; Groundwater; groundwater level; Machine learning; multi objective regression; Multiple objective analysis; Neural networks; Optimization; planning and risk assessment; Predictive models; Random forests; Regression models; Regression tree analysis; Resource management; Soil; Soil layers; Soils; Support vector machines; Time factors; Voting; Water hardness; Water levels; water resource management; Water resources; Weighted voting classifier
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2021.3133889

Water is an essential source of life for every living thing, and drilling is the only source to gain water from underground. Different advanced technologies have been used to minimize the time factor and labor force. Along with technology to be used, some other factors are equally essential to be considered, like water level, the hardness level of the land, and the number of days spent on the whole process. The study proposed a weighted voting classifier based on Differential Evaluation (DE) to classify the regions with different soil colors and land layers. The weights are assigned to the candidate classifiers based on their performance for each class. For the assignment of the optimal weight, the DE optimization algorithm is used. Moreover, the study presents a chained multi-objective regression model to simultaneously predict the water level and total depth on different locations. The proposed work facilitates the drilling industry to increase the rate of penetration (ROP) by selecting the region with soft soil and land layer. The prediction of depth and water level allows the industry to estimate water levels in different areas at different depths. The dataset is provided by the research organization, which contains information of different drilling points. The results of the proposed weighted voting classifier are compared with the traditional machine learning models (kernel Naive Bayes, Gaussian SVM, Quadratic SVM, and Bilayered Neural network) and state of the art voting classifier in terms of precision, recall, and accuracy. Moreover, the proposed regression model is evaluated by well-known evaluation metrics, including Mean Absolute Error, Mean Square Error, and R2 score. Finally, the comparison verifies the effectiveness of the enhanced optimization-based classifier and multi-objective regressor.