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Jenny E. Goldstein Laura Graham Sofyan Ansori Yenni Vetrita Andri Thomas Grahame Applegate Andrew P. Vayda Bambang H. Saharjo Mark A. Cochrane 《Singapore journal of tropical geography》2020,41(2):190-208
Near-annual landscape-scale fires in Indonesia's peatlands have caused severe air pollution, economic losses, and health impacts for millions of Southeast Asia residents. While the extent of fires across the peatland surface has been widely attributed to widespread peatland drainage for plantation agriculture, fires that transition from surface into sub-surface soil-based fires are the source of the most dangerous air pollution. Yet the mechanisms by which this transition occurs have rarely been considered, particularly in diversely managed landscapes. Integrating physical geography methods, including active fire scene evaluations and hydrological monitoring, with qualitative methods such as retrospective fire scene evaluations and semi-structured interviews, this article discusses how and why sub-surface peat fire transition occurs in an intensively altered peatland ecosystem in Indonesia's Central Kalimantan province. We demonstrate that variable water table levels and flammable surface vegetation (fire fuels) are co-produced socio-political and biophysical phenomena that enable the conditions in which surface fire is likely to transition into peat fire and increase landscape vulnerability to ongoing, uncontrollable annual fires. This localized understanding of peat fire transition counters normative causal narratives of tropical fire such as ‘slash-and-burn’, with implications for the management of new fire regimes in inhabited landscapes. 相似文献
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Although Genetic Algorithms have found many successful applications in the field of exploration geophysics, the convergence speed remains a big challenge as Genetic Algorithms usually require a huge amount of fitness function evaluations. In this paper, we propose an efficiency-improved Genetic Algorithm, which has both a good global search capability and a good local search capability, and is also capable of robustly handling the premature convergence challenge commonly seen in linear and directed non-linear optimization methods. In our new genetic algorithm, the global search capability is performed via a modified island model, while the local search capability is provided by a novel self-adaptive differential evolution fine tuning scheme. Premature convergence is dealt with via a local exhaustive search method. We first demonstrate the much improved convergence speed of this efficiency-improved Genetic Algorithm over that of our previously proposed advanced Genetic Algorithm on several multimodal functions. We further demonstrate the effectiveness of our efficiency-improved Genetic Algorithm by applying it to a two-dimensional common reflection surface stacking problem, which is a highly nonlinear geophysical optimization problem, to obtain very encouraging results. 相似文献
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