a watershed management program is usually based on the results of watershed modeling. accurate modeling results are decided by the appropriate parameters and input data. precipitation is the most important input for watershed modeling. precipitation characteristics usually exhibit significant spatial variation, even within small watersheds. therefore, properly describing the spatial variation o...

Abstract This article was conducted to perform a temporal and spatial analysis in order identify suitable climatic regions for tourism. We investigated tourism climate conditions Fars province from 2006 2016 using index (TCI). Also, modified inverse distance weighting (IDW) interpolation is applied generate the optimal pattern of TCI distribution. The relationship between accuracy critical IDW ...

The combination of model predictive control (MPC) and learning methods has been gaining increasing attention as a tool to systems that may be difficult model. Using MPC function approximator in reinforcement (RL) is one approach reduce the reliance on accurate models. RL dependent exploration learn, currently, simple heuristics based random perturbations are most common. This paper considers va...

Fuzzy set theory has shown potential for reducing uncertainty as a result of data sparsity and also provides advantages quantifying gradational changes like those pollutant concentrations through fuzzy clustering based approaches. The ability to lower the sampling frequency perform laboratory analyses on fewer samples, yet still produce an adequate distribution map, would reduce initial cost ne...

Various studies are currently underway on PM (Particulate Matter) monitoring in view of the importance air quality public health management. Spatial interpolation has been used to estimate concentrations due that it can overcome shortcomings station-based and provide spatially continuous information. However, is affected by a combination several factors, only considers spatial relationship betw...