Golestan-FSM dataset

Golestan flood mapping datase

Forest fire susceptibility mapping

The aim of the present study is to develop an accurate approach for analyzing and predicting spatial patterns of forest fire danger with a case study of tropical forest fire in the south west of Iran.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

An evolutionary approach to formulate the compressive strength of roller compacted concrete pavement

© 2019 Elsevier Ltd The construction and maintenance of roads pavement was a critical problem in the last years. Therefore, the use of roller-compacted concrete pavement (RCCP) in road problems is widespread. The compressive strength (fc) is the key characteristic of the RCCP caused to significant impact on the cost of production. In this study, an evolutionary-based algorithm named gene expression programming (GEP) is implemented to propose novel predictive formulas for the fc of RCCP. The fc is formulated based on important factor used in mixture proportion in three different combinations of dimensional form (coarse aggregate, fine aggregate, cement, pulverized fly ash, water, and binder), non-dimensional form (water to cement ratio, water to binder ratio, coarse to fine aggregate ratio and pulverized fly ash to binder ratio) and percentage form of input variables. A comprehensive and reliable database incorporating 235 experimental cases collected from several studies. Furthermore, mean absolute error (MAE), root mean square error (RMSE), correlation coefficient (r), average absolute error (AAE), performance index (PI), and objective function (OBJ) as the internal standard statistical measures and external validation evaluated proposed GEP-based models. Uncertainty and parametric studies were carried out to verify the results. Moreover, sensitivity analysis to determine the importance of each predictor on fc of RCCP revealed that fine aggregate content and water to binder ratio is the most useful predictor in dimensional, non-dimensional and percentage forms, respectively. The proposed equation-based models are found to be simple, robustness and straightforward to utilize, and provide consequently new formulations for fc of RCCP

Towards an integrative, spatially-explicit modeling for flash floods susceptibility mapping based on remote sensing and flood inventory data in Southern Caspian Sea Littoral, Iran

The goal of this study is mapping flood risks over Golestan province in Iran using one of the artificial intelligence methods called multivariate adaptive regression splines (MARS). In this sense, 14 flood conditioning factors were considered and the maps were made in ArcGIS. Additionally, two novel metaheuristic algorithms namely cat swarm optimization (CSO) and water cycle algorithm (WCA) were applied to optimized the MARS parameters. According to the results, the area under the curve provided by receiver operating characteristic curve illustrated the accuracy of 94.5% for the integrated MARS-WCA. As regards the MARS-WCA model, a total area of 44.74% was identified as highly susceptible for flooding. In addition, to determine the maximum influence of input variables on mapping flood risks, the sensitivity analysis was performed. By performing sensitivity analysis, altitude and slope with NDVI were the three important variables, respectively, for spatially flash flood prediction