Soil moisture forecasting by a hybrid machine learning technique: ELM integrated with ensemble empirical mode decomposition

• Published in: Geoderma Vol. 330; pp. 136 - 161
• Main Authors: Prasad, Ramendra, Deo, Ravinesh C., Li, Yan, Maraseni, Tek
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 15.11.2018
• Subjects: algorithms; artificial intelligence; Australia; automation; basins; CEEMDAN; climate; decision making; Drought-prone Murray-Darling Basin; EEMD; ELM; Extreme learning machine; Hybrid models; monitoring; planning; prediction; Random forest; Soil moisture forecasting; soil water; time series analysis; Australia; Extreme learning machine; Hybrid models; EEMD; CEEMDAN; ELM; Soil moisture forecasting; Drought-prone Murray-Darling Basin; Random forest

Soil moisture (SM) is an essential component of the environmental and the agricultural system. Continuous monitoring and forecasting of soil moisture is a desirable strategy to understand the soil dynamics for proactive planning and decision-making measures for agriculture and related fields. In thi...