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All Journal Jurnal Ilmu Lingkungan Jurnal Akuakultur Sungai dan Danau Asian Journal of Aquatic Sciences Jurnal RIVDA
Rizka Aprisanti
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Agriculture, Biological Sciences & Forestry Decision Sciences, Operations Research & Management Environmental Science Law, Crime, Criminology & Criminal Justice Public Health Social Sciences Other

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STRUKTUR KOMUNITAS DIATOM EPILITIK PERAIRAN SUNGAI SENAPELAN DAN SUNGAI SAIL, KOTA PEKANBARU. Rizka Aprisanti; Aras Mulyadi; Sofyan Husein Siregar
Jurnal Ilmu Lingkungan Vol 7, No 2 (2013): Jurnal Ilmu Lingkungan
Publisher : Program Pascasarjana Universitas Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31258/jil.7.2.p.241-252

Abstract

The aims of this research to assess the environmental condition of the Senapelan river and Sail river waters by the species abundance of epilithic diatom. This research identified 14 genera of epilithic diatom. The abundance of epilithic diatom at Senapelan river 48.779cell/cm2 and Sail river 15.564 cell/cm2. The value of diversity indices (H '), Senapelan river(2.936) and Sail river (2.336), the value of equability indices (E '), Senapelan river (0.691)and Sail river (0.550), and the dominant indices (C'), Senapelan river (0.187) and the Sailriver (0.336). Based on these indices Senapelan river and Sail river were included intomoderate polluted waters. Species that dominate Senapelan river and Sail river waters ofPekanbaru City are Navicula sp., Nitzchia sp, Fragilaria sp. and Melosira sp. 
Kontribusi Ekosistem Alam dalam Meningkatkan Ketahanan Ekonomi dan Pangan Nasional di Era Perubahan Iklim yang Dinamis Aprisanti, Rizka; El Fajri, Nur; Budijono
JURNAL RISET INOVASI DAERAH Vol. 3 No. 1 (2025): PELALAWAN DISTRICT RESEARCH AND INNOVATION JOURNAL
Publisher : Badan Riset dan Inovasi Daerah Kabupaten Pelalawan

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Abstract

Global climate change has had a significant impact on human life, especially in social and economic aspects. The economic resilience of communities, especially those that depend on natural resources, is becoming increasingly vulnerable due to the increase in extreme weather events, environmental degradation, and production uncertainty. In the midst of these conditions, natural ecosystems have an important role as a life buffer as well as a support for economic activity. This research aims to assess the contribution of natural ecosystems in supporting economic resilience in the era of climate change. Through this approach, researchers can synthesize various scientific findings to comprehensively explain the relationship between ecosystems and community economic resilience. This method is also useful in developing an analytical framework that will be used to discuss phenomena critically and systematically. The results of the study show that ecosystem services such as resource provision (water, food, energy), disaster management, carbon storage, and ecotourism, have high economic value and have a direct impact on community economic stability. However, ongoing damage to ecosystems threatens the sustainability of these functions. Therefore, the conservation of natural ecosystems needs to be an integral part of economic development and climate change adaptation strategies. Policy strengthening, community involvement, and the application of an ecosystem-based approach are key steps in realizing sustainable economic resilience. The policy implication of this research is the importance of integrating ecosystem service values into economic development planning, especially in the formulation of policies that support environmental protection while improving community welfare.
WATER QUALITY MANAGEMENT TRANSFORMATION THROUGH DEEP LEARNING: FROM LABORATORY TO LARGE-SCALE IMPLEMENTATION (OCEAN) Nur, M Irsyad; Aprisanti, Rizka; Kurniawan, Ronal; Yulindra, Ade; Azuga, Nabila Afifah; Kholis, M Natsir; Limbong, Irwan
Asian Journal of Aquatic Sciences Vol. 8 No. 1 (2025): April
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat Universitas Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31258/ajoas.8.1.102-109

Abstract

The exponential growth of environmental challenges, particularly those affecting water resources, necessitates innovative technological interventions beyond conventional approaches. This review explores the transformative potential of deep learning technologies in water quality management across different scales from controlled laboratory environments to complex oceanic systems. By analyzing recent developments, we identify how neural networks, especially convolutional and recurrent architectures, have revolutionized water quality parameter prediction, anomaly detection, and ecosystem monitoring. Integrating multi-modal data streams with advanced algorithms has enabled unprecedented predictive accuracy and real-time assessment capabilities, transforming reactive monitoring systems into proactive management frameworks. Despite significant progress, challenges remain in data standardization, model interpretability, and the practical deployment of these technologies in resource-constrained settings. This review critically assesses current research trajectories and identifies promising avenues for future development, emphasizing the importance of interdisciplinary collaboration in translating laboratory innovations to large-scale implementation for safeguarding our most precious resource
Analisis Spasial Untuk Memprediksi Risiko Bencana Banjir di Daerah Aliran Sungai Lubis, Syahnan Aly; Reynaldi, Ilham; Zakiah, Zakiah; Aprisanti, Rizka
Jurnal Akuakultur Sungai dan Danau Vol 10, No 2 (2025): Oktober
Publisher : Universitas Batangahari Jambi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33087/akuakultur.v10i2.270

Abstract

Land use change significantly alters the hydrological characteristics of watersheds, especially in rapidly urbanizing river basins like the Citarum, Indonesia. This study aimed to assess the impact of land cover transformation on flood risk by integrating spatial modeling using Cellular Automata–Artificial Neural Network (CA–ANN) with hydrological simulation via HEC-HMS. Multi-temporal Landsat-8 imagery (2014–2024) was used to classify and project land cover to 2029, while Curve Number (CN) values derived from land use types were employed to estimate surface runoff. The results indicated a substantial increase in built-up areas, particularly in the midstream and downstream regions, replacing agricultural lands and reducing vegetative cover. This shift significantly raised CN values across most sub-watersheds, resulting in increased peak discharge, especially in Cikeruh, Cikapundung, and Cibeet. Flood risk mapping showed that over 50% of the sub-watersheds fall into the moderate to high-risk categories, driven by impervious surface expansion and declining infiltration capacity. This integrated spatial–hydrological approach underscores the importance of adaptive land use planning and watershed-based flood mitigation strategies. The findings offer a scientific basis for ecosystem-based disaster risk reduction and inform policy-making in flood-prone urbanizing basins
Co-Authors Aras Mulyadi Budijono, Budijono Irwan Limbong Lubis, Syahnan Aly M Irsyad Nur, M Irsyad Muhammad Natsir Kholis Nabila Afifah Azuga Nuraini El Fajri Reynaldi, Ilham Ronal Kurniawan Sofyan Husein Siregar Yulindra, Ade Zakiah Zakiah