Assessing rainfall prediction models: Exploring the advantages of machine learning and remote sensing approaches
Using a comparison of three different major types, the best predictive model was determined. Statistical models and machine learning algorithms automatically learn and improve based on data. Deep learning uses neural networks to learn complex data patterns and relationships. A combination of satelli...
Published in: | Alexandria Engineering Journal |
---|---|
Main Author: | Latif S.D.; Alyaa Binti Hazrin N.; Hoon Koo C.; Lin Ng J.; Chaplot B.; Feng Huang Y.; El-Shafie A.; Najah Ahmed A. |
Format: | Review |
Language: | English |
Published: |
Elsevier B.V.
2023
|
Online Access: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85173857230&doi=10.1016%2fj.aej.2023.09.060&partnerID=40&md5=cd370191babf611b33890ad186710d8c |
Similar Items
-
Analysing the variability of non-stationary extreme rainfall events amidst climate change in East Malaysia
by: Ng, et al.
Published: (2024) -
Estimation of missing streamflow data using various artificial intelligence methods in peninsular Malaysia
by: Ng, et al.
Published: (2024) -
Enhanced Daily Reference Evapotranspiration Estimation Using Optimized Hybrid Support Vector Regression Models
by: Yong, et al.
Published: (2024) -
Assessing the Efficiency of Remote Sensing and Machine Learning Algorithms to Quantify Wheat Characteristics in the Nile Delta Region of Egypt
by: Elmetwalli A.H.; Mazrou Y.S.A.; Tyler A.N.; Hunter P.D.; Elsherbiny O.; Yaseen Z.M.; Elsayed S.
Published: (2022) -
Dominant Tree Species Classification using Remote Sensing Data and Object -Based Image Analysis
by: Jamal J.; Zaki N.A.M.; Talib N.; Saad N.M.; Mokhtar E.S.; Omar H.; Latif Z.A.; Suratman M.N.
Published: (2022)