Nur Ain Jamal
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Nur Ain Jamal has a total of 3 co-authors. Here are the top co-authors by number of shared publications:
- Gurawan Jati Wibowo
Civil Engineering Department, Universitas Muhamamdiyah Surakarta, Jl. Kebangkitan Nasional No.101, Penumping, Kec. Laweyan, Kota Surakarta 57141, Jawa Tengah, Indonesia
1 shared publication - Izzati Amani Mohd Jafri
Faculty of Civil Engineering & Technology, Universiti Malaysia Perlis, Arau 02600, Perlis, Malaysia
1 shared publication - Norazian Mohamed Noor
Sustainable Environment Research Group (SERG), Centre of Excellence Geopolymer and Green Technology (CEGeoGTech), Universiti Malaysia Perlis, Arau 02600, Perlis, Malaysia
1 shared publication
Latest Publications
- Open Access
- 6 March 2025
- Published by MDPI AG in Environmental and Earth Sciences Proceedings
- Vol. 33 (1) , 5
Assessment of Moving Average (MA) Method for Rainfall Prediction in Yogyakarta, Indonesia
This study investigated a time series model using a moving average (MA) for predicting rainfall trend in seven areas (Panggang, Gedangan, Kedung Keris, Ngawen, Wanagama, Tepus, and Playen) located in the Gunung Kidul Province, Yogyakarta, Indonesia. A database with daily rainfall data covering the period of 2010–2019 obtained from the Central Statistical Body of Gunungkidul District (BPS), Indonesia, were analysed. In this study, the MA was developed using Microsoft Excel. Six performance indicators, including Root Mean Square Error (RMSE) and Index of Agreement (IA), were used to evaluate the goodness-of-fit of the time series model. The results specify that the MA is a reliable model, with the RMSE and IA values in the ranges of 10.7–18.3 and 0.80–0.85, respectively. Small error and high agreement rates between the observed and predicted values indicates that prediction using MA method has good potential to be used as one of the prediction tools for rainfall modelling.
- 6 March 2025
- Published by MDPI AG in Environmental and Earth Sciences Proceedings
- Vol. 33 (1) , 5
Open AccessAssessment of Moving Average (MA) Method for Rainfall Prediction in Yogyakarta, Indonesia
This study investigated a time series model using a moving average (MA) for predicting rainfall trend in seven areas (Panggang, Gedangan, Kedung Keris, Ngawen, Wanagama, Tepus, and Playen) located in the Gunung Kidul Province, Yogyakarta, Indonesia. A database with daily rainfall data covering the period of 2010–2019 obtained from the Central Statistical Body of Gunungkidul District (BPS), Indonesia, were analysed. In this study, the MA was developed using Microsoft Excel. Six performance indicators, including Root Mean Square Error (RMSE) and Index of Agreement (IA), were used to evaluate the goodness-of-fit of the time series model. The results specify that the MA is a reliable model, with the RMSE and IA values in the ranges of 10.7–18.3 and 0.80–0.85, respectively. Small error and high agreement rates between the observed and predicted values indicates that prediction using MA method has good potential to be used as one of the prediction tools for rainfall modelling.