[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125522-en":3,"doc-seo-125522-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125522,7971461741311,"Ophelia","https://ap-avatar.wpscdn.com/avatar/74000253aff267980c6?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779345379180704826",8,"Research & Report","Machine Learning Model for Predicting Net Environmental Effects","Environmental sustainability is a global challenge intensified by disasters that affect communities worldwide. Accurate prediction of net environmental effects requires models that jointly consider environmental and social drivers rather than relying solely on traditional assessment practices. This study proposes a proof-of-concept machine learning approach using synthetic data and a multiple linear regression model with nine features. The model is trained on 1,000 generated samples and achieves an R-squared of 0.67, with renewable energy usage and public awareness emerging as key positive influences. Validation via residual and feature-importance checks indicates reasonable linear-regression performance. Limitations include dependence on synthetic data, linearity assumptions, and a restricted feature set. Results support environmentally focused policymaking and suggest future work using real-world data, non-linear modeling, and expanded factors.","Journal of Informatics and Web Engineering  \nVol. 4 No. 1 (February 2025) eISSN: 2821-370X  \nMachine Learning Model for Predicting Net Environmental Effects  \nSellappan Palaniappan1*, Rajasvaran Logeswaran2, Shapla Khanam3, Zhang Yujiao4,5  \n1Corporate Office, HELP University, No. 15, Jalan Sri Semantan 1, Off Jalan Semantan, Bukit Damansara 50490 Kuala Lumpur, Malaysia  \n2,3Faculty of Computing and Digital Technology, HELP University, Persiaran Cakerawala, Subang Bestari, 40150 Shah Alam, Selangor, Malaysia  \n4Malaysia University of Science and Technology, Block B, Encorp Strand Garden Office, No. 12, Jalan PJU 5/5, Kota Damansara, 47810 Petaling Jaya, Malaysia.  \n5Shaanxi Xueqian Normal University, Shenhe 2nd Road, Chang'an, Xi'an, 710100 Shaanxi, China  \n*corresponding author: ([sellappan.p@help.edu.my](sellappan.p@help.edu.my), ORCiD: 0009-0009-1168-2864)  \nAbstract-Environmental sustainability is a global challenge in the face of increasing incidences of disasters affecting communities worldwide. This requires predicting net environmental effects accurately. While various approaches exist, we need more sophisticated prediction models that account for both environmental and social factors. This study presents a proof-of-concept machine learning model for predicting net environmental effects using synthetic data. We developed a multiple linear regression model incorporating nine key features: renewable energy usage, carbon emissions, air quality index, water usage, biodiversity impact, land use, public awareness, and environmental attitudes. We generated a synthetic dataset of 1000 samples using probability distributions and correlation structures derived from environmental literature and expert knowledge. Our model achieved an Rsquared value of 0.67, demonstrating moderate predictive power. Feature importance analysis revealed renewable energy usage (coefficient = 0.71) and public awareness (coefficient = 0.44) as significant positive factors influencing environmental outcomes. Model validation included residual analysis and feature importance assessment, with results suggesting reasonable performance within linear regression constraints. Limitations of our study include reliance on synthetic data, assumption of linear relationships between variables, and limited environmental factors. Notwithstanding, our findings provide insights for environmental policymaking, particularly regarding renewable energy adoption and public awareness campaigns. Future work could focus on incorporating real-world data, exploring non-linear modeling approaches, and expanding the feature set to capture more complex environmental interactions. Our research contributes to data-driven environmental assessment by demonstrating the feasibility of combining both physical and social factors in predictive modeling.  \nKeywords—Environmental Impact Assessment, Machine Learning, Sustainability Metrics, Predictive Modeling, Synthetic Data  \nReceived: 03 September 2024; Accepted: 28 November 2024; Published: 16 February 2025 This is an open access article under the CC BY-NC-ND 4.0 license.  \n1. INTRODUCTION  \nEnvironmental degradation from human activities poses significant challenges to global sustainability efforts. Recent years have witnessed unprecedented environmental disasters, with extreme weather patterns causing substantial damage to communities, infrastructure, and ecosystems worldwide [1] . The IPCC's latest report highlights increasing  \nfrequencies of floods, landslides, storms, and droughts, underscoring the urgent need for proactive environmental management strategies [2] .  \nTraditional approaches to environmental impact assessment rely heavily on manual data collection and basic modeling techniques. These methods often lack the accuracy, scalability, and adaptability required for comprehensive assessment in today's complex environmental landscape. Machine learning offers promising solutions for modeling these complex relationships and handl","cbCaintKb5RsQ2H3","https://ap.wps.com/l/cbCaintKb5RsQ2H3","pdf",653563,1,11,"English","en",105,"# Introduction\n## Literature Review","[{\"question\":\"What problem does the study address?\",\"answer\":\"The study addresses the need to predict net environmental effects accurately in the context of increasing environmental disasters. It emphasizes accounting for both environmental and social factors.\"},{\"question\":\"How is the predictive model built and what features are used?\",\"answer\":\"The model uses multiple linear regression with nine key features: renewable energy usage, carbon emissions, air quality index, water usage, biodiversity impact, land use, public awareness, and environmental attitudes.\"},{\"question\":\"What performance and validation results are reported?\",\"answer\":\"The synthetic-data-based model achieves an R-squared value of 0.67, indicating moderate predictive power. Validation includes residual analysis and feature importance assessment to confirm reasonable behavior within linear regression constraints.\"}]","Machine Learning Model for Predicting Net Environmental Effects | PDF",1785899608,28,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-model-for-predicting-net-environmental-effects","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-model-for-predicting-net-environmental-effects/125522/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the study address?","Question",{"text":75,"@type":76},"The study addresses the need to predict net environmental effects accurately in the context of increasing environmental disasters. It emphasizes accounting for both environmental and social factors.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is the predictive model built and what features are used?",{"text":80,"@type":76},"The model uses multiple linear regression with nine key features: renewable energy usage, carbon emissions, air quality index, water usage, biodiversity impact, land use, public awareness, and environmental attitudes.",{"name":82,"@type":73,"acceptedAnswer":83},"What performance and validation results are reported?",{"text":84,"@type":76},"The synthetic-data-based model achieves an R-squared value of 0.67, indicating moderate predictive power. Validation includes residual analysis and feature importance assessment to confirm reasonable behavior within linear regression constraints.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]