n this work, the main aim is to map the potential zones of groundwater in Central Highlands (Vietnam) using a novel ensemble machine learning model, namely CG-LMT, which is a combination of two advanced techniques, namely Cascade Generalization (CG) and Logistics Model Trees (LMT). For this, a total of 501 wells data and a set of twelve affecting factors were gathered and selected to generate training and testing datasets used for building and validating the model.
Nội dung trích xuất từ tài liệu:
Groundwater potential zoning using Logistics Model Trees based novel ensemble machine learning model
Groundwater potential zoning using Logistics Model Trees based novel ensemble machine learning model
Số trang: 10
Loại file: pdf
Dung lượng: 12.76 MB
Lượt xem: 10
Lượt tải: 0
Thông tin tài liệu:
Tìm kiếm theo từ khóa liên quan:
Logistics model tree Cascade generalization Machine learning Groundwater potential mapping Cascade generalizationTài liệu có liên quan:
-
9 trang 192 0 0
-
Nuclear energy system's behavior and decision making using machine learning
8 trang 118 0 0 -
95 trang 74 0 0
-
Ebook Disrupting finance: FinTech and strategy in the 21st century
194 trang 58 0 0 -
Bài giảng Trí tuệ nhân tạo dành cho mọi người - ThS. Nguyễn Ngọc Tú
149 trang 58 0 0 -
Knowledge management system building blocks
12 trang 50 0 0 -
Ebook The data science design manual: Part 2
240 trang 48 0 0 -
Building credit scoring process in Vietnamese commercial banks using machine learning
14 trang 47 0 0 -
Phát hiện tin giả với python và machine learning
14 trang 46 0 0 -
Pseudorandom sequences classification algorithm
8 trang 44 0 0
