INTEGRATING ARTIFICIAL INTELLIGENCE AND SATELLITE TELEMETRY INTO COASTAL CONSERVATION AND MARINE POLLUTION LEGISLATION: A FEASIBLE LEGISLATIVE MODEL FOR SRI LANKA

Abstract

The vulnerability of island nations to catastrophic marine pollution and severe coastal degradation - exemplified by the MV X-Press Pearl disaster in Sri Lankan waters - reveals fundamental structural limitations in traditional environmental governance. Sri Lanka’s primary legislative frameworks, the Coast Conservation and Coastal Resource Management Act No. 57 of 1981 (as amended) and the Marine Pollution

Prevention Act No. 35 of 2008, depend primarily on post- hoc reactive physical inspections and static, periodic

planning cycles. This study formulates a feasible legal model that integrates Artificial Intelligence (AI), Synthetic Aperture Radar (SAR) telemetry, multispectral optical remote sensing, and automated data fusion into Sri Lanka’s statutory framework. Through an analytical examination of domestic legislation, international conventions (UNCLOS, MARPOL 73/78, Basel Convention), and recent environmental jurisprudence, this paper identifies critical statutory lacunae regarding data mandates, cross-border intelligence exchange, and the evidentiary admissibility of autonomous algorithmic detections. To resolve these deficiencies, this study proposes two statutory draft amendments: (1) Section

12A of Act No. 57 of 1981, mandating an automated, real- time "Smart Coastal Zone Management System" (Smart- CZMP); and (2) Section 25A of Act No. 35 of 2008,

establishing statutory rebuttable presumptions of liability derived from validated AI satellite surveillance alongside procedural compliance with the Evidence (Special Provisions) Act No. 14 of 1995. This legal framework provides a proactive, technologically sovereign mechanism to mitigate marine plastic accumulation, coastal erosion, and maritime environmental crimes.