Prediction of daily rainfall is important for flood forecasting, reservoir operation, and many other hydrological applications. The artificial intelligence (AI) algorithm is generally used for stochastic forecasting rainfall which is not capable to simulate unseen extreme rainfall events which become common due to climate change. A new model is developed in this study for prediction of daily rainfall for different lead times based on sea level pressure (SLP) which is physically related to rainfall on land and thus able to predict unseen rainfall events. Daily rainfall of east coast of Peninsular Malaysia (PM) was predicted using SLP data over the climate domain. Five advanced AI algorithms such as extreme learning machine (ELM), Bay

New complexes were synthesized with Schiff base tetradentate ligand (L). The ligand was synthesized by the condensation reaction of the dimedone with 2-hydroxybenzohydrazide. The formula of complexes [M(L) (H2O)2].Cl2, where M represents Mn(II), Ni(II) Cu(II), [Co(L)Cl.H2O]Cl and [Zn(L)(H2O)2]Cl2.2H2O. The ligand was identified using m.p., UV-Vis, FT-IR, Mass, 1H-NMR, and C.H.N. These complexes were characterized using techniques including infrared, UV-Vis absorption, magnetic susceptibility, molar conductivity, elemental analyses, thermogravimetric analysis (TGA), chloride content determination using Mohr’s method, and atomic absorption spectroscopy. The measurements revealed that the complexes are electrolytic. FT-IR results dem