Prediction of snow water equivalent using artificial neural network and adaptive neuro-fuzzy inference system with two sampling schemes in semi-arid region of Iran

Direct measurement of snow water equivalent(SWE)in snow-dominated mountainous areas is difficult,thus its prediction is essential for water resources management in such areas.In addition,because of nonlinear trend of snow spatial distribution and the multiple influencing factors concerning the SWE spatial distribution,statistical models are not usually able to present acceptable results.Therefore,applicable methods that are able to predict nonlinear trends are necessary.In this research,to predict SWE,the Sohrevard Watershed located in northwest of Iran was selected as the case study.Database was collected,and the required maps were derived.Snow depth(SD)at 150 points with two sampling patterns including systematic random sampling and Latin hypercube sampling(LHS),and snow density at 18 points were randomly measured,and then SWE was calculated.SWE was predicted using artificial neural network(ANN),adaptive neuro-fuzzy inference system(ANFIS)and regression methods.The results showed that the performance of ANN and ANFIS models with two sampling patterns were observed better than the regression method.Moreover,based on most of the efficiency criteria,the efficiency of ANN,ANFIS and regression methods under LHS pattern were observed higher than the systematic random sampling pattern.However,there were no significant differences between the two methods of ANN and ANFIS in SWE prediction.Data of both two sampling patterns had the highest sensitivity to the elevation.In addition,the LHS and the systematic random sampling patterns had the least sensitivity to the profile curvature and plan curvature,respectively.

Effects of type, level and time of sand and gravel mining on particle size distributions of suspended sediment

Monitoring the sediment transport behavior induced by different interventions, particularly sand mining from rivers, is needed to adaptively manage the watersheds. The particle size distribution of the sus-pended sediment in up and downstream of rivers is one of the main indicators to know about fate of sediments, which may be varied in different conditions. We investigated the effect of some types of sand and gravel (i.e., manual and low, semi-heavy, and heavy machinery) mining on particle size distribution of suspended sediment in the Vaz-e-Owlya, Vaz-e-Sofla and Alesh-Roud riverine mines located in Ma-zandaran Province, northern Iran. The study was conducted on a monthly basis from February, 2012 to January, 2013. Laser granulometry was used to analyze the particle size distribution of suspended se-diment samples taken from up and downstream sections of the study mines. The results revealed that the level and intensity of mining activity affected particle size distribution of suspended sediments. Further statistical assessments in up and downstream sections of the mines proved that sorting, D50, mean, D90, kurtosis, skewness and D10 of the suspended sediment were not significantly influenced by mining activities at levels of 0.09, 0.11, 0.12, 0.15 to 0.69, 0.15–0.69, 0.77, 0.87, 0.97, respectively. While it was not statistically significant, we found that the type of mine and the level of the exploitation changed the particle size distribution of the suspended sediment.

Assessing the health and ecological security of a human induced watershed in central iran

Assessment of the watershed health and associated ecological security is crucial for the proper land resources management,notably when sufficient money and time have lacked.The present study aimed to prepare health and ecological security for the Pishkuh Watershed in Yazd Province,central Iran.To prepare the health atlas of the Pishkuh Watershed,the conceptual framework of pressure,state,response(PSR)was employed.The pressure index was investigated by analyzing the driving forces of natural and human-induced factors.Then,the existing conditions of the natural environment and the watershed performance were analyzed using the state index.Besides,the response index was also calculated as a criterion for expressing the degree of community response or different watershed outcomes to the driving forces imposed on the watershed system.The study results revealed that some 91 and 9%of the watershed were classified as moderately healthy and relatively unhealthy.The results of the ecological security index further showed that some 41%of the area was categorized as relatively poor status.The results of the current study can be used by local managers and decision-makers to adaptively designate appropriate,economic,and goal-targeted solutions for the resources management in the region.