New formulations for prediction of velocity at limit of deposition in storm sewers based on a stochastic technique.

AbstractSedimentation in storm sewers strongly depends on velocity at limit of deposition. This study provides application of a novel stochastic-based model to predict the densimetric Froude number in sewer pipes. In this way, the generalized likelihood uncertainty estimation (GLUE) is used to develop two parametric equations, called GLUE-based four-parameter and GLUE-based two-parameter (GBTP) models to enhance the prediction accuracy of the velocity at the limit of deposition. A number of performance indices are calculated in training and testing phases to compare the developed models with the conventional regression-based equations available in the literature. Based on the obtained performance indices and some graphical techniques, the research findings confirm that a significant enhancement in prediction performance is achieved through the proposed GBTP compared with the previously developed formulas in the literature. To make a quantified comparison between the established and literature models, an index, called improvement index (IM), is computed. This index is a resultant of all the selected indices, and this indicator demonstrates that GBTP is capable of providing the most performance improvement in both training () and testing () phases, comparing with a well-known formula in this context

New stochastic modeling strategy on the prediction enhancement of pier scour depth in cohesive bed materials

Abstract Scouring around the piers, especially in cohesive bed materials, is a fully stochastic phenomenon and a reliable prediction of scour depth is still a challenging concern for bridge designers. This study introduces a new stochastic model based on the integration of Group Method of Data Handling (GMDH) and Generalized Likelihood Uncertainty Estimation (GLUE) to predict scour depth around piers in cohesive soils. The GLUE approach is developed to estimate the related parameters whereas the GMDH model is used for the prediction target. To assess the adequacy of the GMDH-GLUE model, the conventional GMDH and genetic programming (GP) models are also developed for evaluation. Several statistical performance indicators are computed over both the training and testing phases for the prediction accuracy validation. Based on the attained numerical indicators, the proposed GMDH-GLUE model revealed better predictability performance of pier scour depth against the benchmark models as well as several gathered literature studies. To provide an informative comparison among the proposed techniques (i.e. GMDH-GLUE, GMDH, and GP models), an improvement index () is employed. Results indicated that the GMDH-GLUE model achieved = 6% and = 3%, demonstrating satisfying performance improvement in comparison with the previously proposed GMDH model

A novel simulation–optimization strategy for stochastic‐based designing of flood control dam: A case study of Jamishan dam

This study presents a novel stochastic simulation–optimization approach for optimum designing of flood control dam through incorporation of various sources of uncertainties. The optimization problem is formulated based on two objective functions, namely, annual cost of dam implementation and dam overtopping probability, as those are the two major concerns in designing flood control dams. The nondominated solutions are obtained through a multi-objective particle swarm optimization (MOPSO) approach. Results indicate that stochastic sources have a significant impact on Pareto front solutions. The distance index (DI) reveals the rainfall depth (DI = 0.41) as the most significant factor affecting the Pareto front and the hydraulic parameters (DI = 0.02) as the least. The dam overtopping probability is found to have a higher sensitivity to the variability of stochastic sources compared to annual cost of dam implementation. The values of interquartile range (IQR) indicate that the dam overtopping probability is least uncertain when all stochastic sources are considered (IQR = 0.25%). The minimum annual cost of dam implementation (2.79 M$) is also achieved when all stochastic sources are considered in optimization process. The results indicate the potential of the proposed method to be used for better designing of flood control dam through incorporation of all sources of uncertainty

Application of nature-inspired optimization algorithms to ANFIS model to predict wave-induced scour depth around pipelines

Wave-induced scour depth below pipelines is a physically complex phenomenon, whose reliable prediction may be challenging for pipeline designers. This study shows the application of adaptive neuro-fuzzy inference system (ANFIS) incorporated with particle swarm optimization (ANFIS-PSO), ant colony (ANFIS-ACO), differential evolution (ANFIS-DE) and genetic algorithm (ANFIS-GA) and assesses the scour depth prediction performance and associated uncertainty in different scour conditions including live-bed and clear-water. To this end, the non-dimensional parameters Shields number (θ), Keulegan–Carpenter number (KC) and embedded depth to diameter of pipe ratio (e=D) are considered as prediction variables. Results indicate that the ANFIS-PSO model (R 2 live bed ¼ 0:832 and R 2 clear water ¼ 0:984) is the most accurate predictive model in both scour conditions when all three mentioned non-dimensional input parameters are included. Besides, the ANFIS-PSO model shows a better prediction performance than recently developed models. Based on the uncertainty analysis results, the prediction of scour depth is characterized by larger uncertainty in the clear-water condition, associated with both model structure and input variable combination, than in live-bed condition. Furthermore, the uncertainty in scour depth prediction for both live-bed and clear-water conditions is due more to the input variable combination (R-factor ave ¼ 4:3) than it is due to the model structure (R-factor ave ¼ 2:2)

Impact of meteorological drought on vegetation in non-irrigated lands

Drought is a natural phenomenon that causes a lot of damages annually in various sectors, including agriculture and natural resources. The aim of this study is to evaluate the influence of meteorological drought index on vegetation index. For this purpose, the standard precipitation index (SPI) as a meteorological drought index is calculated using the precipitation data of 28 meteorological stations located on the area of Lorestane province, Iran, during the years 1987–2017. Then, the vegetation condition index (VCI) is computed using normalized difference vegetation index (NDVI) images that obtained from MODIS images of Terra satellite during 2000–2017. Dry, normal, and wet years were obtained based on the SPI results for 2008, 2013, and 2016, respectively. SPI and VCI were correlated using Pearson's correlation method. The results of the relationship between VCI and SPI showed that the highest Pearson correlation coefficient related to 9-month SPI in November was equal to 0.64. Multivariate linear regression was also performed between SPI and VCI, and the results showed that SPI was significantly correlated with VCI at 5% level over a period of 9 and 12 months. Finally, a confusion matrix was used to evaluate the compliance of the SPI and VCI drought classes. Results showed that the VCI had the highest compliance in the moderate drought class with SPI

Application of newly developed ensemble machine learning models for daily suspended sediment load prediction and related uncertainty analysis

Ensemble machine learning models have been widely used in hydro-systems modeling as robust prediction tools that combine multiple decision trees. In this study, three newly developed ensemble machine learning models, namely gradient boost regression (GBR), AdaBoost regression (ABR) and random forest regression (RFR) are proposed for prediction of suspended sediment load (SSL), and their prediction performance and related uncertainty are assessed. The SSL of the Mississippi River, which is one of the major world rivers and is significantly affected by sedimentation, is predicted based on daily values of river discharge (Q) and suspended sediment concentration (SSC). Based on performance metrics and visualization, the RFR model shows a slight lead in prediction performance. The uncertainty analysis also indicates that the input variable combination has more impact on the obtained predictions than the model structure selection

Development of Advanced Computer Aid Model for Shear Strength of Concrete Slender Beam Prediction

High-strength concrete (HSC) is highly applicable to the construction of heavy structures. However, shear strength (Ss) determination of HSC is a crucial concern for structure designers and decision makers. The current research proposes the novel models based on the combination of adaptive neuro-fuzzy inference system (ANFIS) with several meta-heuristic optimization algorithms, including ant colony optimizer (ACO), differential evolution (DE), genetic algorithm (GA), and particle swarm optimization (PSO), to predict the Ss of HSC slender beam. The proposed models were constructed using several input combinations incorporating several related dimensional parameters such as effective depth of beam (d), shear span (a), maximum size of aggregate (ag), compressive strength of concrete (fc), and percentage of tension reinforcement (ρ). To assess the impact of the non-homogeneity of the dataset on the prediction result accuracy, two possible modeling scenarios, (i) non-processed (initial) dataset (NP) and (ii) pre-processed dataset (PP), are inspected by several performance indices. The modeling results demonstrated that ANFIS-PSO hybrid model attained the best prediction accuracy over the other models and for the pre-processed input parameters. Several uncertainty analyses were examined (i.e., model, variables, and data), and results indicated predicting the HSC shear strength was more sensitive to the model structure uncertainty than the input parameters

Prediction of channel sinuosity in perennial rivers using Bayesian Mutual Information theory and support vector regression coupled with meta-heuristic algorithms

Support Vector Regression (SVR) combined with Invasive Weeds Optimization (IWO), standalone SVR, and Radial Basis Function Neural Networks are applied to estimate channel sinuosity in perennial rivers. With this aim, a dataset with 132 sinuosity data and related geomorphologic data, corresponding to 119 perennial streams, is considered. Bayesian Mutual Information theory is used to determine the parameters affecting channel sinuosity to reveal that bankfull depth affects sinuosity the most. Seven input parameter combinations for sinuosity prediction are considered, and in both training and testing stages, the SVR-IWO model (RTrain=0.959,RMSETrain=0.072,MAETrain=0.037,Rtest=0.892,RMSETest=0.103,MAETest=0.065) shows the best prediction performance while the standalone SVR model generated the results with performances of (RTrain=0.792,RMSETrain=0.158,MAETrain=0.141,Rtest=0.704,RMSETest=0.163,MAETest=0.151). Model prediction uncertainty is quantified in terms of entropy for the three models considered, further confirming that the sinuosity set predicted by the SVR-IWO model is the closest to the observed set

The socioeconomic impact of severe droughts on agricultural lands over different provinces of Iran

The lack of rainfall is the primary cause of drought, reduced crop harvest (CH), and socioeconomic drought. Agriculture is the primary source of income for most Iranians, and drought can harm people's lives irreparably. This study examines the changes in the CHs and crop prices (CPs) across provinces of Iran during the most severe drought of all time in Iran and its impact on producers (farmers), consumers, and public prosperity using the Surplus Economic Method (SEM). Our study focused on crops that have a big impact on Iranian life, such as wheat, barley, potato, onion, tomato, lentils, chickpeas, and alfalfa. Our results indicated that Iran's most severe hydrological drought occurred from 2000 to 2002. The rainfed farms experienced the most pronounced changes in terms of CH, while financial damages were the highest in irrigated areas. Among the crops investigated, rainfed wheat has experienced the greatest reduction of 80%. Moreover, grains have the greatest price change (40% increase) during the hydrological drought. Wheat underwent the steepest CH reduction. Legumes experienced the steepest price rise. During the drought, most crops had lower yields, causing losses for consumers, but some producers still made a profit. The drought affected northwest and west Iran farmers adversely, but southern and central Iran farmers gained from the drought through increased product prices. Drought has had adverse effects on the public prosperity for most of the examined crops and reduced it. The greatest reduction corresponds to barley in the western regions and the Zagros Mountains. The diversity of crops in northwestern and western Iran has made these regions the most important areas for farming and crop supply in Iran. Agricultural droughts in these regions can affect the lives of all Iranian people and lead to socioeconomic drought in Iran. Our study demonstrated that hydrological droughts in northwestern and western areas of Iran are chiefly caused by the shortage of winter and spring rains. Moreover, Identifying the primary factors of hydrological drought showed that the hydrological drought is most affected by the depth of snow during winter. Additionally, the data analysis revealed that the combined effect of winter precipitation with snow depth and snow depth with snow coverage has the highest impact on hydrological drought (61%). The results can make farming policies based on the region and climate, marketing plans for droughts, and solutions to address the harmful effects of drought