Comparative Case Study of Rainfall-Runoff Modeling between SWMM and Fuzzy Logic Approach

Wang, Keh‐Han; Altunkaynak, Abdüsselam

doi:10.1061/(asce)he.1943-5584.0000419

Cited by 71 publications

(33 citation statements)

References 30 publications

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“…Deng et al [18] used fuzzy number scales with pair-wise comparisons for solving decision problems involving qualitative data very effectively in Australia. Two of the fuzzy pair-wise comparisons and FOWA were used for different water resource assessments, such as prioritizing the restoration strategies for Lake Urmia, Iran to avoid shrinkage [17], evaporation estimation [19,20], water consumption prediction [21], rainfall-runoff forecasting and modelling [22][23][24], and evaluation of groundwater pollution using GWQI [25].To assess water quality, various multivariate statistical analyses were successfully applied in many previous studies, such as groundwater modelling using the principal component analysis (PCA) technique [26][27][28]. However, PCA can only reduce the dimensionality of large data sets based on the variation of variables in the new coordinate axis and the modelling approach required detailed data [28,29].…”

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Groundwater Quality Assessment Using Fuzzy-AHP in An Giang Province of Vietnam

et al. 2019

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Along with rapid population growth in Vietnam, there is an increasing dependence on groundwater for various activities. An Giang province is known to be one of the agricultural intensification areas of The Vietnamese Mekong Delta (VMD). This study aimed to evaluate the spatiotemporal variation of groundwater quality for a period of ten years from 2009 to 2018 in An Giang. The weighted groundwater quality index (GWQI) was developed based on the fuzzy analytic hierarchy process (Fuzzy-AHP) for assigning weighted parameters. The results show that that shallow wells in the Northeast and Southeast regions of An Giang were mostly categorized under "bad water" quality with high arsenic (As) concentration over the years partly due to huge amounts of sediment deposition in monsoon season. Overall, the reason for the poor groundwater quality in An Giang was the combined effect of both natural and human activities. On the other hand, we detected high values of GWQI links with high As concentration in areas where people extract more groundwater for irrigation. Temporal variation of GWQI suggested that groundwater quality at eight wells has improved from 2009 to 2018 in the wet season as compared to the dry season. The reason behind the improvement of groundwater quality during wet season was the decrease in river discharge, which causes less deposition of suspended solids near the flood plains. Moreover, the filling of unused wells can reduce the movement of pollutants from unused wells to groundwater aquifers. Although there was not sufficient evidence to show the relationship between As and sediment concentration, the temporal reduction trend in river discharge and suspended solids was detected in An Giang. The understanding of groundwater quality can help policymakers protect and manage limited water resources in the long-term.Geosciences 2019, 9, 330 2 of 23 of usages [6,7]. The disadvantages of the AHP method include the uncertainty and ambiguity in expressing opinions, as the method depends on the decision maker's knowledge and experiences during the decision-making process. Moreover, among other factors, the AHP method does not contain feedback loops [8].The fuzzy set was first developed by Zadeh in 1965 [5] and combined with Saaty's priority theory to reduce human ambiguity [9,10]. Later, the Fuzzy-AHP was further developed in order to overcome the uncertainty and ambiguity of criteria weights in deterministic and inflexible classifications [11]. Using the Fuzzy-AHP can provide a fuzzy number-interval judgment values rather than fixed or exact values [2]. This approach reduces uncertainty in assigned relative weight. As a result, the Fuzzy-AHP has been successfully used in many actual decision situation making such as energy alternatives selection [12,13], supplier selection [14], environmental sustainability evaluation [7], and water quality assessment [15][16][17]. Baghapour et al. [15] conducted the Fuzzy-AHP with fuzzy ordered weighted averaging (FOWA) for developing of the groundwater quality index (GWQI). They...

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Groundwater Quality Assessment Using Fuzzy-AHP in An Giang Province of Vietnam

et al. 2019

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“…SWMM developed by EPA (Environmental Protection Agency, USA) is a dynamic rainfallrunoff simulation model that computes runoff originating primarily from urban areas from single or continuous events (Huber and Dickinson 1988;Rossman 2005). It consists of different blocks to be simulated separately (Wang and Altunkaynak 2012). Blocks used in the current study are the runoff block for runoff estimation and transport block for routing of the estimated runoff.…”

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Role of rainfall events and imperviousness parameters on urban runoff modelling

Sahoo

Pekkat

2013

ISH Journal of Hydraulic Engineering

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The volume and rate of storm water runoff in an urban watershed depends directly on the precipitation magnitude as well as its spatial and temporal distribution over the catchment. The runoff simulation may be on the basis of continuous or event wise rainfall data. Event wise simulation requires a minimum of one design-storm hyetograph as input. In contrast, continuous runoff modeling incorporates the entire meteorological record (days, months or years) of the watershed as the input. Many a time, due to the non-availability of short interval rainfall data, a continuous simulation is performed. The present study predicts the runoff in an urban North East Indian catchment based on event wise and continuous simulation. Continuous modeling has been performed by taking the daily rainfall data as input file. Storm water management model has been used for runoff simulation. This would help to understand the variations in urban runoff modeling due to an event based and continuous rainfall input. Green Ampt model has been used for infiltration modeling. A comparison is presented between the predicted runoff from the continuous and event wise rainfall data by considering different imperviousness parameters. The simulated runoff is overestimated by the total impervious area (TIA) that disregards the type of connectivity to the collecting network. IntroductionRunoff simulation using event wise rainfall data requires a minimum of one design-storm hyetograph as input. This type of hydrologic modeling reveals how a basin responds to an individual rainfall event (e.g. quantity of surface runoff, peak, timing of the peak, detention). It is particularly useful for understanding detailed hydrologic processes like infiltration and surface runoff in a particular watershed (Daniel et al. 2010). In contrast, continuous hydrologic modeling incorporates the entire meteorological record (days, months or years) of the watershed as the input and synthesizes hydrologic processes and phenomena (i.e. synthetic responses of the basin to a number of rain events and their cumulative effects) over a longer time period that includes both wet and dry conditions (Chu and Steinman 2009). Many a time due to lack of event wise rainfall data, simulation using continuous rainfall data is performed. Therefore, there is a need to evaluate the effect of continuous and event wise simulation on runoff prediction in an urban catchment, where imperviousness will be high.Irrespective of the type of simulation, the runoff in urban catchment is largely affected by imperviousness due to urbanization. Imperviousness being a direct measure of urbanization, information regarding it is very much essential for urban hydrology and watershed management (Snyder et al. 2005). Imperviousness may be of two types based on its connectivity to the collecting network, such as directly connected or effective impervious area (EIA) and non-effective impervious area (NEIA). Together they constitute total impervious area (TIA). EIA is that portion of TIA that has a direct hydraulic con...

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“…One of the most popular uses of data‐driven models is the prediction of runoff from rainfall, e.g. by means of developing a neural network (De Vos, ), fuzzy logic (Wang and Altunkaynak, ) or genetic programming (Rodríguez‐Vázquez et al ., ) solution that will effectively convert observed input into required output. In this application domain, the importance of capturing the spatial variability of rainfall‐runoff processes via distributed and semi‐distributed hydrological modelling frameworks is well‐known (Beven and O'Connell, ; Tetzlaff and Uhlenbrook, ; Segond et al ., ; Younger et al ., ).…”

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Including spatial distribution in a data-driven rainfall-runoff model to improve reservoir inflow forecasting in Taiwan

et al. 2012

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Multi‐step ahead inflow forecasting has a critical role to play in reservoir operation and management in Taiwan during typhoons as statutory legislation requires a minimum of 3‐h warning to be issued before any reservoir releases are made. However, the complex spatial and temporal heterogeneity of typhoon rainfall, coupled with a remote and mountainous physiographic context, makes the development of real‐time rainfall‐runoff models that can accurately predict reservoir inflow several hours ahead of time challenging. Consequently, there is an urgent, operational requirement for models that can enhance reservoir inflow prediction at forecast horizons of more than 3 h. In this paper, we develop a novel semi‐distributed, data‐driven, rainfall‐runoff model for the Shihmen catchment, north Taiwan. A suite of Adaptive Network‐based Fuzzy Inference System solutions is created using various combinations of autoregressive, spatially lumped radar and point‐based rain gauge predictors. Different levels of spatially aggregated radar‐derived rainfall data are used to generate 4, 8 and 12 sub‐catchment input drivers. In general, the semi‐distributed radar rainfall models outperform their less complex counterparts in predictions of reservoir inflow at lead times greater than 3 h. Performance is found to be optimal when spatial aggregation is restricted to four sub‐catchments, with up to 30% improvements in the performance over lumped and point‐based models being evident at 5‐h lead times. The potential benefits of applying semi‐distributed, data‐driven models in reservoir inflow modelling specifically, and hydrological modelling more generally, are thus demonstrated. Copyright © 2012 John Wiley & Sons, Ltd.

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Comparative Case Study of Rainfall-Runoff Modeling between SWMM and Fuzzy Logic Approach

Cited by 71 publications

References 30 publications

Groundwater Quality Assessment Using Fuzzy-AHP in An Giang Province of Vietnam

Groundwater Quality Assessment Using Fuzzy-AHP in An Giang Province of Vietnam

Role of rainfall events and imperviousness parameters on urban runoff modelling

Including spatial distribution in a data-driven rainfall-runoff model to improve reservoir inflow forecasting in Taiwan

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