UM Research Repository
metadata only
Enhancing the performance of data-driven models for monthly reservoir evaporation prediction
The accuracy level for reservoir evaporation prediction is an important issue for decision making in the water resources field. The traditional methods for evaporation prediction could encounter numerous obstacles owing to the effect of several parameters on the shape of the evaporation pattern. The
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Coastal water level prediction: a comparative study of statistical and machine learning techniques for time series forecasting#316255
- Developing reservoir evaporation predictive model for successful dam management#1241790
- Estimating Groundwater Level Records Using MOVE.1 and Computing Monthly Percentiles From Estimated Groundwater Records in Massachusetts#621863
- Machine Learning-Based Assessment of Long-Term Groundwater Level Dynamics in the Nakhon Luang Aquifer from 1978 to 2023.#1001033
- T520: Fundamentals of Groundwater Modelling — Defra Commercial — Groundwater Science Ltd#656335
Record · ID 1278329
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive