2016
DOI: 10.13057/biodiv/d170234
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Long-term variability of zooplankton community under climate warming in tropical eutrophic man-made lake

Abstract: Sunardi, Yoshimatsu T, Junianto N, Istiqamah N, DeWeber T. 2016. Long-term variability of zooplankton community under climate warming in tropical eutrophic man-made lake. Biodiversitas 17: 626-633. The climate warming is increasingly acknowledged as an important driver of lake ecosystems. However, there are no generic patterns of how the aquatic species/community responds the warming climate; instead the changes are complicated by interactions of many factors. To regard the important role of zooplankton in the… Show more

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Cited by 4 publications
(6 citation statements)
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“…Early efforts in this regard utilized the Long-range Energy Alternatives Planning System (LEAP) model [8] and were further refined with expert judgment [9]. Other effective models include the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model [10], the Back Propagation Neural Network (BPNN) model [11,12], the Integrated MARKAL-EFOM System (TIMES) model for industry-level predictions [13,14], the Particle Swarm Optimization (PSO) algorithm [12,15], the system dynamic model [16], and the Verhulst Grey forecasting (V-GM) model for country-level predictions [17]. These studies emphasize the utility of these models and demonstrate relatively high levels of accuracy in their results.…”
Section: The Related References 21 the Related Researches For The Cem...mentioning
confidence: 99%
See 1 more Smart Citation
“…Early efforts in this regard utilized the Long-range Energy Alternatives Planning System (LEAP) model [8] and were further refined with expert judgment [9]. Other effective models include the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model [10], the Back Propagation Neural Network (BPNN) model [11,12], the Integrated MARKAL-EFOM System (TIMES) model for industry-level predictions [13,14], the Particle Swarm Optimization (PSO) algorithm [12,15], the system dynamic model [16], and the Verhulst Grey forecasting (V-GM) model for country-level predictions [17]. These studies emphasize the utility of these models and demonstrate relatively high levels of accuracy in their results.…”
Section: The Related References 21 the Related Researches For The Cem...mentioning
confidence: 99%
“…However, it is worth noting that only a few studies have attempted to address data uncertainty issues by narrowing down the list of input factors, although most studies have pointed out that the selected factors primarily relate to energy efficiency and production in the cement industry [11][12][13][14][15][16][17].…”
Section: The Related References 21 the Related Researches For The Cem...mentioning
confidence: 99%
“…Early attempts have been made with the Long-range Energy Alternatives Planning System (LEAP) model and improved with expert judgement [28,33]. Some other effective models include the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model [34], the Back Propagation Neural Network (BPNN) [9,29], the Integrated MARKAL-EFOM System (TIMES) model for industry-level prediction [21,35], the Particle Swarm Optimization (PSO) algorithm [30,31], the system dynamic model [37], and the Verhulst Grey forecasting (V-GM) model for country-scale prediction [36]. The mentioned studies show relatively high accuracy in their results by promoting the models.…”
Section: Literature Reviewmentioning
confidence: 99%
“…The model performed well with high accuracy TIMES [21] China's cement industry Multi: production, process, energy, economic Determined factor: cement production LEAP [33] Canadian cement industry Multi: energy, production, technology Determined factor: the penetration rate of the energy efficiency PSO-BP [31] China's cement industry 44 scenarios on the technology used in production line. MAPE = 2.92%, R 2 = 0.9999 PSO [30] U.S., China, and Japan Total emissions data MAPE = 7.9995% STIRPAT [34] China's reginal sectors Multi: natural and socio-economic factors R 2 = 0.984 V-GM [36] China's cement industry Multi: production, process, energy MAPE = 2.71%, R 2 = 0.9729 TIMES [35] Swiss cement industry Multi: technological and economic factors Average error rate = 2.01% System dynamic [37] Indonesian cement industry Multi: socio-economic, production, energy, technology Determined factor: cement production…”
Section: Yearmentioning
confidence: 99%
“…To overcome this problem, the efficient development and utilization of the city can be promoted by integrating the concerns of various stakeholders [24]. AHP can well solve the problem of multi-stakeholder index construction [25]. In 2023, Adhi and Muslim analyzed and studied fuzzy AHP for owners, contractors, consultants, subcontractors, architects, government, local government and NGOs, and other stakeholders of the gap between the analysis and research.…”
Section: Introductionmentioning
confidence: 99%