Применение искусственных нейронных сетей для прогноза нормализованного вегетационного индекса (NDVI) биоклиматических ландшафтов бассейна реки Дияла
Аннотация
Данное исследование касается перспектив использования искусственных нейронных сетей для прогнозирования распределений Normalized Differential Vegetation Index (NDVI) в бассейне реки Дияла и главным образом того, каким образом информация о типах биоклиматических ландшафтов повлияет на прогнозируемость NDVI. Для этого в первом этапе эксперимента на вход персептронов с одним скрытым слоем и различными функциями активации подавались только общегеографические характеристики одного из 25 000 участков бассейна размером 0,05° × 0,05° (широта и долгота, минимальная, средняя и максимальная высота над уровнем моря, тип земного покрова) и сезонные метеорологические факторы (сумма осадков и средние температуры, минимальные и максимальные температуры) и прогнозировалось значение NDVI в начале вегетационного периода. Все 20 персептронов с 4–20 скрытыми узлами обучались на данных 2000–2010 гг. с помощью алгоритма обратного распространения ошибки и тестировались на данных за 2011–2016 гг. Было показано, что лучшее соответствие между прогнозируемым и фактическими NDVI с коэффициентом детерминации (КД), равным 0,78, достигается персептроном с логистической функцией активации 12 скрытых нейронов и гиперболической тангенциальной активацией выходного нейрона. При этом обнаружена пространственная неоднородность качества прогноза: в верховьях реки, характеризуемых согласно Кеппену – Треварта, как ландшафты умеренного горного климата и субтропического климата с сухим летом, КД = 0,76–0,80, тогда как в сухих степных ландшафтах и полупустынных ландшафтах низовий реки КД = 0,59–0,66. Эксперименты с 20 моделями с добавлением типа ландшафтов на вход персептронов показали возможное улучшение КД на 5 %, а индивидуальный подбор модели персептронов для каждого ландшафтов (всего 150 моделей) позволил увеличить КД до 0,73–0,85. Тем не менее сильное отличие характеристик индивидуальных моделей осложняет перспективы их использования в практических целях и требует поиска новых подходов.
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DOI: http://dx.doi.org/10.14529/ctcr190301
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