ارزیابی مزایای استفاده از یک سیستم تخصصی پشتیبانی تصمیم گیری زنجیره عقب مانده در پیش بینی سیل محلی و هشدار Evaluation of the benefits of using a backward chaining decision support expert system for local flood forecasting and warning

1 | INTRODUCTION Flood incidents can endanger human life, cause extensive property damage, and result in significant harm to the environment. To attenuate the risk and reduce the loss caused by flood accidents, flood forecasting has been studied and developed throughout human history. Although global or nationwide flood forecasts and warnings are available through mass media, the comparatively low accuracy of prediction for a certain region causes false alarms, improper responses, and therefore unnecessary loss of property and/or life. One conventional method to improve the accuracy is to increase the resolution or decrease the based cluster size. Either way, the occupancy of computational resources must be increased enormously. The higher the resolution and the smaller the cluster size, the more computing power is needed. Another alternative method is to develop stand‐alone systems only for small regions. Recent examples include using ensemble numerical weather prediction systems for medium‐range flood forecasting (Cloke & Pappenberger, 2009); applying data‐driven approaches, such as traditional artificial neural networks, adaptive neuro‐fuzzy inference systems, wavelet neural networks, and hybrid adaptive neuro‐fuzzy inference systems with multiresolution analysis using wavelets to develop models for hourly run‐off forecasting at Casino station on the Richmond River in Australia (Badrzadeh, Sarukkalige, & Jayawardena, 2015); coupling meteorological observations and forecasts with a distributed hydrological model to advance flood forecasting in Alpine watersheds (Jasper, Gurtz, & Lang, 2002); coupling HEC‐HMS with atmospheric models for predicting watershed run‐off in California (Anderson, Chen, Kavvas, & Feldmand, 2002); and combining multimodels for operational forecasting for river basins in the Western United States (Najafi & Moradkhani, 2015). Although the models or systems listed above provided overall better performance for the whole river basins or catchments examined in the cited studies, the accuracy of forecasting for a small place such as a small town, a little community, and a specific house was not mentioned or was completely ignored. The reason is the same: To obtain accurate forecasting for a comparatively small place, the resolution of the entire studied region of the river basin or catchment must be higher (Luo, Xu, Jamont, Zeng, & Shi, 2007). More detailed local situations must be collected and considered, more memory space must be allocated, and a heavier computational burden must be loaded onto the models that already have vast amounts of meteorological, hydrologic, and hydraulic data to analyse through complicated calculations (Cloke & Pappenberger, 2009; Fang, Xu, Zhu, et al., 2014b). In fact, most incidents begin and end locally and are managed at the local level (DHS, 2013). The most useful data are locally collected, although it is correlated with the data from outside the specific region.