Flash floods are one of the most devastating natural hazards; they occur within a catchment (region) where the response time of the drainage basin is short. Identification of probable flash flood locations and development of accurate flash flood susceptibility maps are important for proper flash flood management of a region. With this objective, we proposed and compared several novel hybrid computational approaches of machine learning methods for flash flood susceptibility mapping, namely AdaBoostM1 based Credal Decision Tree (ABM-CDT); Bagging based Credal Decision Tree (Bag-CDT); Dagging based Credal Decision Tree (Dag-CDT); MultiBoostAB based Credal Decision Tree (MBAB-CDT), and single Credal Decision Tree (CDT). These models were applied at a catchment of Markazi state in Iran. About 320 past flash flood events and nine flash flood influencing factors, namely distance from rivers, aspect, elevation, slope, rainfall, distance from faults, soil, land use, and lithology were considered and analyzed for the development of flash flood susceptibility maps. Correlation based feature selection method was used to validate and select the important factors for modeling of flash floods. Based on this feature selection analysis, only eight factors (distance from rivers, aspect, elevation, slope, rainfall, soil, land use, and lithology) were selected for the modeling, where distance to rivers is the most important factor for modeling of flash flood in this area. Performance of the models was validated and compared by using several robust metrics such as statistical measures and Area Under the Receiver Operating Characteristic (AUC) curve. The results of this study suggested that ABM-CDT (AUC = 0.957) has the best predictive capability in terms of accuracy, followed by Dag-CDT (AUC = 0.947), MBAB-CDT (AUC = 0.933), Bag-CDT (AUC = 0.932), and CDT (0.900), respectively. The proposed methods presented in this study would help in the development of accurate flash flood susceptible maps of watershed areas not only in Iran but also other parts of the world.

We evaluated salt tolerance in 319 recombinant inbred lines derived from a cross between Roshan and Falat (seri82) in bread wheat (Triticum aestivum L.). This study identified quantitative trait lci (QTLs) with additive (a) and additive×additive (aa) epistatic effects, and characterized their salt treatment interactions (at and aat) at the seedling stage. Using a genetic map of 730 DArT and SSR markers, we identified 65 additive QTLs governing 13 traits using the QTL Cartographer program to assess single-treatment phenotypic values. We further identified 13 additive and 14 epistatic QTLs for 10 traits with the QTLNetwork program using multitreatment phenotypic values. Our results show that four of the additive and seven of the epistatic QTLs exhibited an effect on the response to salt treatment. Morphological traits had less effect on treatment than physiological traits did. Of the three additive QTLs found to affect shoot Na + concentration, two colocalized with loci governing shoot fresh or dry weight (1B-2 and 3B-1). Thirteen pairs of QTLs across five chromosomal groups were detected at homologous positions in the A, B, and D genomes, indicative of synteny.

Background Salinity is one of the major limiting abiotic stresses that decrease crop production worldwide. To recommend genotypes for cultivation under saline stress conditions, a comprehensive understanding of the genetic basis and plant responses to this stress is needed. In the present study, a total of 20 barley genotypes were investigated to identify potential salt-tolerant genotypes, both at the early growth stage using a hydroponic system, and in adult plants under field conditions. For these purposes, the multi-trait genotype-ideotype distance index (MGIDI) was used to identify salt-tolerant barley genotypes at the seedling stage, and the weighted average of absolute scores (WAASB) index was used to identify the high-yielding and stable genotypes in adult plant stage. At the early growth stage, barley seedlings were treated with two salinity levels: 0 mM NaCl (as control conditions) and 200 mM NaCl (as stress conditions) for 30 days, and during this period different growth and physiological traits were measured. Besides, the yield performance and stability of the investigated barley genotypes were evaluated across five environments during the 2018–2020 cropping seasons. Results Salinity stress significantly decreased growth and physiological traits in all seedling plants; however, some salt-tolerant genotypes showed minimal reduction in the measured traits. Multivariate analysis grouped the measured traits and genotypes into different clusters. In the early growth stage, the G12, G14, G6, G7, and G16 were selected as the most salt-tolerant genotypes using MGIDI index. In the multi-environment trials experiment, AMMI analysis showed that grain yields of the tested barley genotypes were influenced by the environment (E), genotype (G), and GE interaction. Based on the weighted average of absolute scores of the genotype index (WAASB) and other stability statistics, G7, G8, G14, and G16 were selected as superior genotypes. Conclusion Together the MGIDI and WAASB indices revealed that three genotypes—G7, G14 and G16—can be recommended as new genetic resources for improving and stabilizing grain yield in barley programs for the moderate climate and saline regions of Iran. Our results suggest that using the MGIDI index in the early growth stage can accelerate screening nurseries in barley breeding programs. Besides, the WAASB index can be used as a useful stability measurement for identify high-yielding and stable genotypes in multi-environment trials.

The salinity tolerance of 17 breeding wheat genotypes along with three local varieties was evaluated under control and salinity stress (160 mM NaCl) conditions. At the seedling stage, shoot and root dry weights, relative water content (RWC), membrane stability index (MSI), relative chlorophyll content (SPAD index), root and shoot Na + (RN and SN), root and shoot K + (RK and SK), root and shoot K + /Na + ratios (RKN and SKN), root-to-shoot Na + translocation (RTSN), root-to-shoot K + translocation (RTSK), stomatal conductance (G S ), transpiration rate (T E ), and photosynthesis rate (P N ) were measured. Moreover, the investigated genotypes were assessed in terms of grain yield across four saline regions during the 2018-2019 cropping seasons. Salinity stress caused a signi cant reduction in the RDW, SDW, PN, G S , T E , SK, RKN, SKN, RTSN, and RTSK, but resulted in increased RN, RK, and SN. The results of AMMI analysis of variance also indicated signi cant differences among test locations, genotypes, and their interaction effects. The PCA-based biplot revealed that grain yield strongly correlated with RKN and RK. Furthermore, the correlation among P N , G S , and T E traits was strong and positive and had a positive correlation with RWC, MSI, RDW, and SPAD index. Considering our results, RK and RKN were identi ed as useful physiological tools to screen salt tolerance at the early-growth stage. According to the ranking patterns obtained by the average sum of ranks method (ASR) and grain yield, we observed that genotype number G5 had considerable physiological potential at the early-growth stage and also responded well to soil salinity at the farm; thus this genotype can be promoted for commercial production.