Application of two fuzzy models using knowledge-based and linear aggregation approaches to identifying flooding-prone areas in Tehran

Flooding is one of the most problematic natural events affecting urban areas. In this regard, developing flooding models plays a crucial role in reducing flood-induced losses and assists city managers to determine flooding-prone areas (FPAs). The aim of this study is to investigate on the prediction capability of fuzzy analytical hierarchy process (FAHP) and Mamdani fuzzy inference system (MFIS) methods as two completely and semi-knowledge-based models to identify FPAs in Tehran, Iran. Six flooding conditioning factors including density of channel, distance from channel, land use, elevation, slope, and water discharge were extracted from various geo-spatial datasets. A total of 62 flooding locations were identified in the study area based on the existing reports and field surveys. Of these, 44 (70%) floods were randomly selected as training data and the remaining 18 (30%) cases were used for the validation purposes. After the data preparation step, data were processed by means of two statistical (FAHP) and soft computing (MFIS) methods. Unlike most statistical and soft computing approaches which use flooding inventory data for both training and evaluation of models, only conditioning factor was involved in data processing and inventory data were used in the current study to assess models prediction accuracy. Also, the efficiency of two approaches was evaluated by pixel matching (PM) and area under curve to validate the prediction capability of models. The prediction rate for MFIS and FAHP was 89% and 84%, respectively. Moreover, according to the results obtained from PM, it was found out that about 90% of known flooding locations fell in high-risk areas, whereas it was 83% for FAHP, indicating that flooding susceptibility map of MFIS has higher performance.

     

Image acquisition for detection of vegetation change based on long-term rainfall in an arid rangeland in Western NSW, Australia

Research on vegetation change, rangeland assessment or desertification modelling in drylands using remotely sensed image acquisition normally ignores long-term rainfall as a key criterion in image acquisition. This article will present a novel procedure for image acquisition to investigate vegetation change in a degraded rangeland located in Western New South Wales (Western NSW) Australia. Western NSW experienced an unusually prolonged period of rainfall deficit during the 2000s compared to the 1970, 1980 and 1990s. For this purpose, vegetation changes were assessed using Landsat images supported by field survey. The long-term rainfall variability (42-year) was regarded as a key element in image acquisition. Within the timeframe of the 2000s, 2 years with 25 % lower than the 42-year mean annual rainfall were selected. These images were then compared to an image captured in a year (1988) with rainfall closer to the 42-year mean annual rainfall. Two change detection techniques were used, namely univariate image differencing and GIS approaches. Classification of the produced images was pursued based on the digital numbers (supervised) of ground-checked points within the reference image whilst considering the histogram (unsupervised) of each digital number of the produced image. This research emphasized rainfall as a key variable in image acquisition for vegetation change analysis in rangelands. Image acquisition based on long-term rainfall data allowed for the assessment of changes in perennial plant cover by eliminating the effects of extreme rainfall variation on annual grass dynamics and removing extreme reflections caused by their temporary high photosynthetic activity.