Description
Abstract
Global warming, driven by fossil fuel combustion, deforestation, and industrial pollution, continues to threaten fragile ecosystems worldwide, with the biodiverse Great Rift Valley in East Africa among the most exposed. Rising temperatures and shifting hydrological regimes have altered lake systems such as Lake Nakuru, whose water levels have risen since 2010, disrupting flamingo habitats. Restoring degraded landscapes at the pace the climate crisis demands requires tools that are precise, scalable, qualities that align with the Industry 4.0+ technologies driving current ecosystem-resilience efforts. This study reviews four Artificial Intelligence (AI)-driven strategies for climate change mitigation and landscape restoration in the region: AI-optimized agroforestry, integrated landscape initiatives, forest fire prediction and detection, and Smart Archive Models for climate data management. AI-optimized agroforestry has increased crop yields by 20 percent in India while sequestering approximately 2 tons of CO2 per hectare annually, offering a model for smallholder farmers across the Rift Valley facing erratic rainfall and declining soil fertility. Integrated landscape initiatives, powered by AI-driven drones, have restored 500,000 hectares of degraded land in East Africa since 2020 and currently monitor over 1 million hectares with 85 percent species-detection accuracy, generating roughly 5,000 rural jobs. Forest fire prediction systems achieve 90 percent forecasting accuracy up to 48 hours in advance, cutting fire-related forest damage by 40 percent. Smart Archive Models categorize climate and environmental datasets with 95 percent accuracy, cutting data-analysis time by 50 percent and unifying fragmented records into a decision-ready resource for policymakers. Drawing on a systematic review of over 200 peer-reviewed studies spanning 2015 to 2024, we show these four strategies function as an integrated system, in which agroforestry data feeds Smart Archives, landscape drones inform fire-risk models, and archived records refine restoration planning, collectively reducing greenhouse gas emissions by 15 to 20 percent while strengthening biodiversity and rural livelihoods. We argue that AI-driven restoration, embedded within circular economy and Industry 4.0+ frameworks, offers a credible pathway toward restoring 10 million hectares of degraded land by 2030, and call on governments and research institutions to invest accordingly, using the Great Rift Valley as a model for technology-enabled restoration worldwide.
| Title | Restoration of Landscapes:The Use of Artificial Intelligence to curb Global warming. |
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