Rainfall forecasting using PSPline and rice production with ocean-atmosphere interaction

2nd International Conference on Eco Engineering Development 2018

生态环境科学

Caraka, Rezzy Eko^1 ; Supatmanto, Budi Darmawan^2 ; Tahmid, Muhammad^3 ; Soebagyo, Joko^4 ; Mauludin, M. Ali^5 ; Iskandar, Akbar^6 ; Pardamean, Bens^1,7

Bioinformatics and Data Science Research Center (BDSRC), Bina Nusantara University, Jakarta

11480, Indonesia^1

Weather Modification Technology Center, Agency for the Assessment and Application of Technology (BPPT), Indonesia^2

Indonesia Agency for Meteorology Climatology and Geophysics, Indonesia^3

College of Technology, Wastukancana, Purwakarta, Indonesia^4

Laboratory of Sociology and Extension, Padjadjaran University, Indonesia^5

Informatics Engineering, STMIK AKBA, Makassar, Indonesia^6

Computer Science Department, BINUS Graduate Program - Master of Computer Science, Bina Nusantara University, Jakarta

11480, Indonesia^7

关键词: Climate anomalies;    Distribution patterns;    Growth and development;    Ocean;    Ocean-atmosphere interactions;    Penalized;    Penalized splines;    Rainfall forecasting;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/195/1/012064/pdf
DOI  :  10.1088/1755-1315/195/1/012064

学科分类：环境科学（综合）

来源: IOP

【 摘 要 】

The role of climate can be affected by plants. The weather can accelerate and multiply the existence of various plant pests and diseases, accelerate the growth and development of grass among plants, and encourage the emergence of infection and significant damage to plants. The elements of climate that affect the growth of plants are one of them is rainfall. In this paper, we performed the simulation using the non-parametric penalized spline (PSPLINE) method and studied the effect on rice production in Lampung. It can be concluded that the increasing fluctuation, frequency, and intensity of climate anomalies in the last decade caused by the ENSO phenomenon have an impact on changes in distribution patterns, intensity, and period of the wet season so that the start of the rainy season and the dry season becomes too late. As a result, there is a seasonal shift from normal average conditions that can ultimately have severe implications for food crops. In a nutshell penalized spline gives high accuracy with R 2 = 96.227% and MAPE = 1.62%.