Landslide susceptibility mapping using Genetic Algorithm for the Rule Set Production(GARP) model

Investigation on landslide phenomenon is necessary for understanding and delineating the landslide prone and safer places for different land use practices. On this basis, a new model known as genetic algorithm for the rule set production was applied in order to assess its efficacy to obtain a better result and a more precise landslide susceptibility map in Klijanerestagh area of Iran. This study considered twelve landslide conditioning factors(LCF) like altitude, slope, aspect, plan curvature, profile curvature, topographic wetness index(TWI), distance from rivers, faults, and roads, land use/cover, and lithology. For modeling purpose, the Genetic Algorithm for the Rule Set Production(GARP) algorithm was applied in order to produce the landslide susceptibility map. Finally, to evaluate the efficacy of the GARP model, receiver operating characteristics curve as well as the Kappa index were employed. Based on these indices, the GARP model predicted the probability of future landslide incidences with the area under the receiver operating characteristics curve(AUC-ROC) values of 0.932, and 0.907 for training and validating datasets, respectively. In addition, Kappa values for the training and validating datasets were computed as 0.775, and 0.716, respectively. Thus, it can be concluded that the GARP algorithm can be a new but effective method for generating landslide susceptibility maps(LSMs). Furthermore, higher contribution of the lithology, distance from roads, and distance from faults was observed, while lower contribution was attributed to soil, profile curvature, and TWI factors. The introduced methodology in this paper can be suggested for other areas with similar topographical and hydrogeological characteristics for land use planning and reducing the landslide damages.

Field evaluation of durum wheat landraces for prevailing abiotic and biotic stresses in highland rainfed regions of Iran

Biotic and abiotic stresses are major limiting factors for high crop productivity worldwide. A landrace collection consisting of 380 durum wheat(Triticum turgidum L. var. durum) entries originating in several countries along with four check varieties were evaluated for biotic stresses:yellow rust(Puccinia striiformis Westendorf f. sp. tritici) and wheat stem sawfly(WSS) Cephus cinctus Norton(Hymenoptera: Cephidae), and abiotic stresses: cold and drought. The main objectives were to(i) quantify phenotypic diversity and identify variation in the durum wheat landraces for the different stresses and(ii) characterize the agronomic profiles of landraces in reaction to the stresses. Significant changes in reactions of landraces to stresses were observed.Landraces resistant to each stress were identified and agronomically characterized.Percentage reduction due to the stresses varied from 11.4%(yellow rust) to 21.6%(cold stress) for 1000-kernel weight(TKW) and from 19.9(yellow rust) to 91.9%(cold stress) for grain yield. Landraces from Asia and Europe showed enhanced genetic potential for both grain yield and cold tolerance under highland rainfed conditions of Iran. The findings showed that TKW and yield productivity could be used to assess the response of durum wheat landraces to different stresses. In conclusion, landraces showed high levels of resistance to both biotic and abiotic stresses, and selected landraces can serve in durum wheat breeding for adaptation to cold and drought-prone environments.