Monthly reservoir inflow forecasting using a new hybrid SARIMA genetic programming approach

Forecasting reservoir inflow is one of the most important components of water resources and hydroelectric systems operation management. Seasonal autoregressive integrated moving average (SARIMA) models have been frequently used for predicting river flow. SARIMA models are linear and do not consider the random component of statistical data. To overcome this shortcoming, monthly inflow is predicted in this study based on a combination of seasonal autoregressive integrated moving average (SARIMA) and gene expression programming (GEP) models, which is a new hybrid method (SARIMA–GEP). To this end, a four-step process is employed. First, the monthly inflow datasets are pre-processed. Second, the datasets are modelled linearly with SARIMA and in the third stage, the non-linearity of residual series caused by linear modelling is evaluated. After confirming the non-linearity, the residuals are modelled in the fourth step using a gene expression programming (GEP) method. The proposed hybrid model is employed to predict the monthly inflow to the Jamishan Dam in west Iran. Thirty years’ worth of site measurements of monthly reservoir dam inflow with extreme seasonal variations are used. The results of this hybrid model (SARIMA–GEP) are compared with SARIMA, GEP, artificial neural network (ANN) and SARIMA–ANN models. The results indicate that the SARIMA–GEP model (R ²=78.8, VAF =78.8, RMSE =0.89, MAPE =43.4, CRM =0.053) outperforms SARIMA and GEP and SARIMA–ANN (R ²=68.3, VAF =66.4, RMSE =1.12, MAPE =56.6, CRM =0.032) displays better performance than the SARIMA and ANN models. A comparison of the two hybrid models indicates the superiority of SARIMA–GEP over the SARIMA–ANN model.     

Predicting the Building Stone Cutting Rate Based on Rock Properties and Device Pullback Amperage in Quarries Using M5P Model Tree

One of the key parameters that affect the selection of equipment and the cost estimation of dimension stone quarries is the rock cutting rate or production rate. In this study, the M5P tree algorithm is used to determine the relationship between the hard rock sawability and its factors especially the physical and mechanical characteristics of rock. To achieve the research goal, a variety of eleven types of hard dimension stone were selected and nine major physical and mechanical characteristics of rock including uniaxial compressive strength, Young’s modulus, Brazilian tensile strength, equivalent quarts content, grain size, Mohs hardness, point load test, density and P-wave velocity of these samples were evaluated. The cutting rate of diamond wire for all of the Workpiece was measured at different pullback amperage with a fully instrumented cutting platform in laboratory. All operational parameters of cutting process were entirely controlled. Thus, a database containing 99 datasets was provided and it has been used for analyses. The obtained results from the pruned and unpruned tree models showed a significant relationship between cutting rate and its factors. In the end, the results of M5P tree method were compared with statistical analyses (i.e., linear and nonlinear regression). The coefficient of determination be equal with 0.92, 0.86, 0.77 and 0.63 for unpruned tree, pruned tree, linear and nonlinear regression method respectively. This comparison showed that the both method of M5P tree technique have a better performance in predicting the cutting rate rather than the statistical regression methods.     

A comparative analysis among computational intelligence techniques for dissolved oxygen prediction in Delaware River

Most of the water quality models previously developed and used in dissolved oxygen (DO) prediction are complex. Moreover, reliable data available to develop/calibrate new DO models is scarce. Therefore, there is a need to study and develop models that can handle easily measurable parameters of a particular site, even with short length. In recent decades, computational intelligence techniques, as effective approaches for predicting complicated and significant indicator of the state of aquatic ecosystems such as DO, have created a great change in predictions. In this study, three different AI methods comprising: (1) two types of artificial neural networks (ANN) namely multi linear perceptron (MLP) and radial based function (RBF); (2) an advancement of genetic programming namely linear genetic programming (LGP); and (3) a support vector machine (SVM) technique were used for DO prediction in Delaware River located at Trenton, USA. For evaluating the performance of the proposed models, root mean square error (RMSE), Nash–Sutcliffe efficiency coefficient (NS), mean absolute relative error (MARE) and, correlation coefficient statistics (R) were used to choose the best predictive model. The comparison of estimation accuracies of various intelligence models illustrated that the SVM was able to develop the most accurate model in DO estimation in comparison to other models. Also, it was found that the LGP model performs better than the both ANNs models. For example, the determination coefficient was 0.99 for the best SVM model, while it was 0.96, 0.91 and 0.81 for the best LGP, MLP and RBF models, respectively. In general, the results indicated that an SVM model could be employed satisfactorily in DO estimation.     

A regional Bayesian hierarchical model for flood frequency analysis

Stochastic Environmental Research and Risk Assessment - In this study, we propose a regional Bayesian hierarchical model for flood frequency analysis. The Bayesian method is an alternative to the...     

Anthropogenic Decline of Ancient,Sustainable Water Systems: Qanats

Qanat is an ancient underground structure to abstract groundwater without the need for external energy. A recognized world heritage, Qanat has enabled civilization in arid and semi-arid regions that lack perennial surface water resources. These important structures, however, have faced significant challenges in recent decades due to increasing anthropogenic pressures. This study uses remote sensing to investigate land-use changes and the loss of 15,983 Qanat shafts in the Mashhad plain, northeast of Iran, during the past six decades. This entails obtaining a rare aerial imagery from 1961, as well as recent satellite imagery, over a region with the highest density of Qanats in Iran, the birthplace of Qanat. Results showed that only 5.59% of the Qanat shafts in 1961 remained intact in 2021. The most prominent Qanat-impacting land-use changes were agriculture and urban areas, that accounted for 42.93 and 31.81% Qanat shaft destruction in the study area, respectively. This study also showed that groundwater table decline, demographic changes, and reduction in the appeal of working in the Qanat maintenance and construction industry among the new generation are existential threats to Qanats, and may result in the demise of these ancient structures in the future. Findings of this study can be used for urban planning in arid and semi-arid areas with the aim of protecting these historic water structures.     

Linearized limb-darkening coefficients for use in analysis of eclipsing binary light curves

Linear limb-darkening coefficientsu required in the analysis of eclipsing binary curves, are tabulated for a wide range of effective temperature (50 000° to 4000°), wavelength (0.2 2.2 ), and gravitiesg (2.0logg5.0). The computation is based on the comprehensive range of model atmospheres of Carbon and Gingerich (1969).The results are compared with the theoretical values of Hosokawa (1957), Kopal (1959) and Grygaret al. (1972), and examined in relation to empirically determined values ofu from analyses of eclipsing binary light curves. An improved agreement between theory and observation for the calculated limb-darkening coefficients of the present work is noted.     

Assessment of climate change impacts on climate variables using probabilistic ensemble modeling and trend analysis

Water resources in snow-dependent regions have undergone significant changes due to climate change. Snow measurements in these regions have revealed alarming declines in snowfall over the past few years. The Zayandeh-Rud River in central Iran chiefly depends on winter falls as snow for supplying water from wet regions in high Zagrous Mountains to the downstream, (semi-)arid, low-lying lands. In this study, the historical records (baseline: 1971–2000) of climate variables (temperature and precipitation) in the wet region were chosen to construct a probabilistic ensemble model using 15 GCMs in order to forecast future trends and changes while the Long Ashton Research Station Weather Generator (LARS-WG) was utilized to project climate variables under two A2 and B1 scenarios to a future period (2015–2044). Since future snow water equivalent (SWE) forecasts by GCMs were not available for the study area, an artificial neural network (ANN) was implemented to build a relationship between climate variables and snow water equivalent for the baseline period to estimate future snowfall amounts. As a last step, homogeneity and trend tests were performed to evaluate the robustness of the data series and changes were examined to detect past and future variations. Results indicate different characteristics of the climate variables at upstream stations. A shift is observed in the type of precipitation from snow to rain as well as in its quantities across the subregions. The key role in these shifts and the subsequent side effects such as water losses is played by temperature.

     

Late glacial and early Holocene hydroclimate variability in northwest Iran (Talesh Mountains) inferred from chironomid and pollen analysis

We reconstructed the paleohydrologic and climatic history of the Lake Neor region, NW Iran, from the end of the late glacial to the middle Holocene (15,500–7500 cal yr BP). Subfossil chironomid and pollen assemblages in a sediment core from a peatland located south of Lake Neor enabled identification of four main hydrologic phases. The period 15,500–12,700 cal yr BP was characterized by a relatively dry climate with an open landscape, suggested by the abundance of Irano-Turanian steppe plants (e.g. Amaranthaceae, Artemisia and Cousinia). Dominance of several shallow-water and semi-terrestrial chironomid taxa (e.g. Pseudosmittia, Smittia/Parasmittia and Paraphaenocladius/Parametriocnemus) during this period is indicative of lower water tables in the wetland. Between 12,700 and 11,300 cal yr BP, chironomid taxa indicate higher wetland water tables, as suggested by the presence of Zavrelia, Chironomus anthracinus/plumosus-type and Micropsectra, which are inhabitants of open-water, lacustrine areas. The open-steppe vegetation remained dominant in the watershed during this time. Increasing wetland moisture could be explained by: (1) cool summers that reduced the evaporation rate; and/or (2) a decrease in duration of the summer dry season. The period 11,300–8700 cal yr BP was characterized by lower wetland moisture, contemporaneous with a delay in the expansion of deciduous forest, suggesting persistent dry climate conditions throughout the beginning of the Holocene, which may have been related to the intensified seasonality of precipitation. Around 8700 cal yr BP, higher wetland water levels, inferred from chironomids, occurred simultaneously with the onset of regional deciduous forest expansion, probably caused by a shortening of the summer dry period. We concluded that chironomids are appropriate paleoecological proxies to investigate global and local hydrologic variability in the Middle East.     

Rain gauge network evaluation and optimal design using spatial correlation approach in arid and semi-arid regions of Iran

Theoretical and Applied Climatology - The present study is aimed at evaluation of a rain gauge network in order to optimize a network design. In this regard, point rainfall estimations were...