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...
الحاوية / القاعدة: | Alexandria Engineering Journal |
---|---|
المؤلف الرئيسي: | Latif S.D.; Alyaa Binti Hazrin N.; Hoon Koo C.; Lin Ng J.; Chaplot B.; Feng Huang Y.; El-Shafie A.; Najah Ahmed A. |
التنسيق: | Review |
اللغة: | English |
منشور في: |
Elsevier B.V.
2023
|
الوصول للمادة أونلاين: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85173857230&doi=10.1016%2fj.aej.2023.09.060&partnerID=40&md5=cd370191babf611b33890ad186710d8c |
مواد مشابهة
-
Assessing seasonal rainfall erosivity variability in East Malaysia: a trend analysis approach
بواسطة: Lam, وآخرون
منشور في: (2025) -
Estimation of missing streamflow data using various artificial intelligence methods in peninsular Malaysia (vol 19, pg 4338, 2024)
بواسطة: Ng, وآخرون
منشور في: (2025) -
Review of environmental monitoring in freshwater lakes using geospatial techniques (remote sensing and GIS)
بواسطة: Kamaruzzaman K.; Salleh S.A.; Pardi F.; Abdullah M.F.; Foronda V.; Bergonio E.L.; Rahmawaty R.
منشور في: (2025) -
LANDSLIDE SUSCEPTIBILITY USING REMOTE SENSING AND GIS: A CASE STUDY IN HULU LANGAT, SELANGOR, MALAYSIA
بواسطة: Syams M.N.E.; Ibrahim I.; Latif N.A.; Asmawi M.Z.; Salleh S.A.
منشور في: (2025) -
Assessing seasonal rainfall erosivity variability in East Malaysia: a trend analysis approach
بواسطة: Lam S.W.; Ng J.L.; Huang Y.F.; Lee J.C.; Lee W.K.
منشور في: (2025)