Mapping Groundwater Potential Using a Novel Hybrid Intelligence Approach
By Shaghayegh Miraki, Sasan Hedayati Zanganeh, Kamran Chapi, Vijay P. Singh, Ataollah Shirzadi, Himan Shahabi, Binh Thai Pham. Water resources management, 2018
184 citations recorded by the source corpus.
Subfields: Environmental Engineering, Global and Planetary Change, Water Science and Technology
View Paper: https://www.semanticscholar.org/paper/73f8b067c2a7ccd27e0ab103a696b34c5726bf8b
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Environmental Engineering
- Ozgur Kisi
- Jan Adamowski
- Kwok-Wing Chau
- Masoud Karbasi
- Ravinesh C. Deo
- Mohammad Zounemat‐Kermani
- C. Wu
- Hossein Bonakdari
In Global and Planetary Change
In Water Science and Technology
- Ozgur Kisi
- Masoud Karbasi
- Jan Adamowski
- Ravinesh C. Deo
- Kwok-Wing Chau
- Mohammad Zounemat‐Kermani
- A. Guven
- Vahid Nourani
Citations in the corpus
- Groundwater-Potential Mapping Using a Self-Learning Bayesian Network Model: A Comparison among Metaheuristic Algorithms (2021, methodology): “In groundwater-potential mapping, the machinelearning models used so far are classified as random forest (RF) [27], support vector machine (SVM) [28], boosted regression trees (BRT) [29], classification and regression trees (CART) [30], linear discriminant analysis (LDA) [31], multivariate adaptive ”