Ahmad Zainal
Institut Teknologi Brunei (ITB)

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AI-POWERED PREDICTIVE MODELING OF FOREST FIRE RISK IN RIAU PROVINCE BASED ON CLIMATE, PEATLAND, AND LAND USE DATA Loso Judijanto; Siti Mariam; Ahmad Zainal
Journal of Selvicoltura Asean Vol. 2 No. 3 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsa.v2i3.2484

Abstract

Forest and peatland fires in Riau Province, Indonesia, are a recurrent environmental disaster with severe regional and global consequences. Traditional fire danger rating systems often fail to capture the complex interplay of factors driving these events. The advancement of artificial intelligence (AI) offers an opportunity to develop more accurate and dynamic fire risk prediction models. This study aimed to develop and validate a high-performance, AI-powered model for predicting daily forest fire risk at a high spatial resolution across Riau Province by integrating climate, peatland, and land use data. We integrated historical satellite-detected fire hotspots (2015-2023) as the dependent variable. Predictor variables included daily climate data (e.g., temperature, precipitation, wind speed), static peatland characteristics (e.g., depth, type), and dynamic land use/land cover data. An XGBoost (Extreme Gradient Boosting) machine learning algorithm was trained to learn the complex, non-linear relationships between these drivers and fire occurrence. The model’s predictive performance was rigorously evaluated using the Area Under the Curve (AUC) metric. The XGBoost model demonstrated high predictive accuracy, achieving an AUC of 0.93. The analysis revealed that the number of consecutive dry days, peatland depth, and proximity to oil palm plantations were the most influential variables in predicting fire risk. The model successfully generated daily 1-km resolution fire risk maps, identifying specific areas with elevated danger. The AI-powered model provides a robust and significantly more accurate tool for forest fire forecasting in fire-prone tropical peatland landscapes. This approach offers a critical advancement for developing effective early warning systems, enabling targeted resource allocation for fire prevention and mitigation efforts.
THE INFLUENCE OF PARENTAL STYLES ON CHILD DEVELOPMENT: A LONGITUDINAL STUDY Ahmed Shah; Ahmad Zainal; Salma Hamdan
Research Psychologie, Orientation et Conseil Vol. 3 No. 1 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/rpoc.v3i1.3538

Abstract

Parental styles play a significant role in shaping the development of children, influencing various aspects of their emotional, cognitive, and social growth. While much research has explored the immediate effects of parenting on children, few studies have examined these effects over an extended period. Understanding how different parental styles impact child development across time is essential for identifying long-term outcomes and informing parenting strategies. This study aims to investigate the long-term influence of different parental styles on child development, focusing on emotional regulation, social competence, and academic achievement. The goal is to explore how these parenting styles affect children at various developmental stages. A longitudinal design was employed, with data collected from 200 families over a span of 10 years. Parenting styles were categorized based on the authoritative, authoritarian, permissive, and uninvolved frameworks. Child development was measured using standardized assessments for emotional regulation, social competence, and academic performance. The findings revealed that children of authoritative parents showed higher levels of emotional regulation, better social skills, and stronger academic performance compared to children of authoritarian, permissive, or uninvolved parents. This study underscores the importance of authoritative parenting in fostering positive long-term outcomes in child development.