http://www.agrimet.ir/article_69418_f4e2bbe100cda91f8c910d33a104a621.pdf

Incomplete rainfall datasets with missing gaps is a major challenge in climatology and water resource studies. In the present study, two intelligent models, namely Genetic Programing (GP) and Support Vector Machines (SVM) were used to reconstruct the monthly rainfall data of four rain-gauges located in Hamedan province, Iran during the period of 1992 to 2011. The incomplete rainfall data was reconstructed first by using the data of one, two and three stations respectively. The results showed that increasing the memory and the number of stations involved in the training phase, will improve the performance of the models. In reconstruction of monthly precipitation data of Sarabi and Maryanj stations, the Support Vector Machine method showed better performance with RMSE of 12.9 mm and 11.4 mm, and correlation coefficients (r) of 0.93 and 0.95, respectively. The corresponding values of RMSE for GP approach were 13 mm and 12.21 mm, which indicated the superior performance of SVM

Ensemble of M5 Model Tree Based Modelling of Sodium Adsorption Ratio

This work reports the results of four ensemble approaches with the M5 model tree as the base regression model to anticipate Sodium Adsorption Ratio (SAR). Ensemble methods that combine the output of multiple regression models have been found to be more accurate than any of the individual models making up the ensemble. In this study additive boosting, bagging, rotation forest and random subspace methods are used. The dataset, which consisted of 488 samples with nine input parameters were obtained from the Barandoozchay River in West Azerbaijan province, Iran. Three evaluation criteria: correlation coefficient, root mean square error and mean absolute error were used to judge the accuracy of different ensemble models. In addition to the use of M5 model tree to predict the SAR values, a wrapper-based variable selection approach using a M5 model tree as the learning algorithm and a genetic algorithm, was also used to select useful input variables. The encouraging performance motivates the use of this technique to predict SAR values

Addressing quality and usability of surface water bodies in semi-arid regions with mining influences

Water resources management has considerable importance, specifically in the context of climate change. This subject has introduced new challenges in semi-arid regions with water quality problems, such as the Iberian Pyrite Belt, which is one of the largest metallogenetic provinces in the world and one of the driest regions in Europe. Positioned in the Mediterranean context, the region has a high density of polymetallic sulphide mines that promote the degradation of water systems. The present study aims to assess the water quality in the Pyrite Belt, considering a total of 34 surface water bodies, including constructed reservoirs, permanent and ephemeral streams, and mining facilities with accumulated water (e.g., pit lakes and mining dams). The water samples were analysed for physico-chemical properties, including field parameters (pH, electrical conductivity), alkalinity/acidity, hardness, anions, and potential toxic elements. The results were used for hydrochemical classifications and the assessment of suitability for public uses. Statistical methods, such as hierarchical cluster analysis and nearest centroid classifier, were used for grouping and evaluating the similarity between water bodies. Two groups were generated from the analysis: i) constructed lakes with alkaline and sodium signatures; and ii) waters suffering from the influence of mining wastes, e.g., showing high acidity, sulphate and metal contents. Therefore, the loss of water quality in the vicinity of mines reflects the impact of acid mine drainage. The methodological approach used may be applied to the integrated management of water resources in regions with mining influences and where it is necessary to combat drought and water scarcity scenarios.Patricia Gomes acknowledge FCT (Science and Technology Foundation, Portugal) by the research fellowship under the POCH (Programa Operacional Capital Humano) supported by the European Social Fund and National Funds of MCTES (Ministerio da Ciencia, Tecnologia e Ensino Superior) with reference SFRH/BD/108887/2015. This work was co-funded by the European Union through the European Regional Development Fund, based on COMPETE 2020 (Programa Operacional da Competitividade e Internacionalizacao) - project ICT (UID/GEO/04683/2013) with reference POCI-01-0145-FEDER-007690 and project Nano-MINENV number 029259

Forecasting of Mean Daily Runoff Discharge of Behesht-Abad River Using Wavelet Analysis

Forecasting of river discharge is a key aspect of efficient water resources planning and management. In this study, two models based on Wavelet Analysis and Artificial Neural networks (ANNs) were developed for forecasting discharge of Behesht-Abad River. For this purpose, mean daily discharge data of mentioned river as well as precipitation data of 17 meteorological stations were used in the period 1999-2008. In the first method, called Cross Wavelet (CW), complex Morlet wavelet was used as analyzer function. Wavelet analyzing was performed for every daily rainfall and average discharge time series, separately. Initial phase, phase differences of subseries obtained from wavelet analysis, and calibration coefficients were calculated. Then structural series were reconstructed and average of structural components calculated. The river discharges were predicted for 1, 2, 3 and 7 days ahead forecasting horizon. In the second method, called Wavelet Neural Networks conjunction (WNN), a preprocessing was done on the initial input matrix using Meyer wavelet. Then the elements of the initial input matrix were normalized and the second input matrix was created. A three layer Feed Forward Back Propagation (FFBP) was formed based on the second input matrix and target matrix. After training the model using Levenberg–Marquardt (LM) algorithm, the river discharges were predicted for short term time horizons. The results showed that the WNN method had higher accuracy in short-term forecasting of river discharge in comparison with CW and ANN methods

Comparison between Genetic Programming and Support Vector Machine Methods for Daily River Flow Forecasting (Case Study: Barandoozchay River)

Accurate estimation of river flow can have a significant importance in water resources management. In this study, Genetic programming (GP) and Support Vector Machine (SVM) methods were used to forecast daily discharge of Barandoozchay River. The daily discharge data of Barandoozchay River measured at the Dizaj hydrometric station during 2007 to 2011 was used for modeling, which 80% of the data used for training and remaining 20% used for testing of models. The results showed that in the both of considered methods, the models including discharges of one, two and three days ago had higher accuracy in verification step and the accuracy of models decreased with increasing discharge values. Comparing the performance of GP and SVM methods indicated that, however the accuracy of the GP method with the R=0.978 and RMSE=1.66 (m3/s) was slightly more than SVM method with R=0.976 and RMSE=1.80 (m3/s), but the SVM is easier than GP method. Thus, the SVM method can be used as an alternative method in forecasting daily river discharge

A New Method for Joint Frequency Analysis of Modified Precipitation Anomaly Percentage and Streamflow Drought Index Based on the Conditional Density of Copula Functions

In this study, a new method was proposed to model the occurrence of related variables based on the conditional density of copula functions. The proposed method was adopted to investigate the dynamics of meteorological and hydrological droughts in the Zarinehroud basin, southeast of Lake Urmia, during the period 1994–2015. For this purpose, the modified precipitation anomaly percentage and streamflow drought indices were extracted. Finally, the joint frequency analysis of duration-duration and severity-severity characteristics of meteorological and hydrological droughts was analyzed. Analysis of 7 different copulas used to create the joint distribution in the Zarinehroud basin indicated that the Frank copula had the best performance in describing the relationship between the meteorological and hydrological drought severities and durations. By examining the results of the bivariate analysis of duration-duration of meteorological and hydrological droughts at different stations, the expected meteorological and hydrological drought durations were estimated in the years ahead. For example, at Chalkhmaz station, 4- to 7-month duration for the hydrological drought and 9- to 12-month duration for the meteorological drought is expected in the years ahead. The joint frequency analysis of drought characteristics allows to determine the meteorological and hydrological drought characteristics at a single station at the same time using joint probabilities. Also, the results indicated that by knowing the conditional density, the hydrological drought characteristics can be easily estimated for the given meteorological drought characteristics. This could provide users and researchers useful information about the probabilistic behavior of drought characteristics for optimal operation of surface water

Analyzing the conditional behavior of rainfall deficiency and groundwater level deficiency signatures by using copula functions

The complex hydrological events such as storm, flood and drought are often characterized by a number of correlated random variables. Copulas can model the dependence structure independently of the marginal distribution functions and provide multivariate distributions with different margins and the dependence structure. In this study, the conditional behavior of two signatures was investigated by analyzing the joint signatures of groundwater level deficiency and rainfall deficiency in Naqadeh sub-basin in Lake Urmia Basin using copula functions. The study results of joint changes in the two signatures showed that a 90-135 mm reduction in rainfall in the area increased groundwater level between 1.2 and 1.7 m. The study results of the conditional density of bivariate copulas in the estimation of groundwater level deficiency values by reducing rainfall showed that changes in values of rainfall deficiency signature in the sub-basin led to the generation of probability curves of groundwater level deficiency signature. Regarding the maximum groundwater level deficiency produced, the relationship between changes in rainfall deficiency and groundwater level deficiency was calculated in order to estimate the groundwater level deficiency signature values. The conditional density function presented will be an alternative method to the conditional return period

Trivariate joint frequency analysis of water resources deficiency signatures using vine copulas

Investigating the interaction of water resources such as rainfall, river flow and groundwater level can be useful to know the behavior of water balance in a basin. In this study, using the rainfall, river flow and groundwater level deficiency signatures for a 60-day duration, accuracy of vine copulas was investigated by joint frequency analysis. First, while investigating correlation of pair-variables, tree sequences of C-, D- and R-vine copulas were investigated. The results were evaluated using AIC, Log likelihood and BIC statistics. Finally, according to the physics of the problem and evaluation criteria, D-vine copula was selected as the best copula and the relevant tree sequence was introduced. Kendall’s tau test was used to evaluate the correlation of pair-signatures. The results of the Kendall’s tau test showed that pair-signatures studied have a good correlation. Using D-vine copula and its conditional structure, the joint return period of groundwater deficiency signature affected by rainfall and river flow deficiency signatures was investigated. The results showed that the main changes in the groundwater level deficiency is between 0.3 and 2 m, which due to the rainfall and the corresponding river flow deficiency, return periods will be less than 5 years. Copula-based simulations were used to investigate the best copula accuracy in joint frequency analysis of the studied signatures. Using copula data of the studied signatures, the groundwater deficiency signature was simulated using D-vine copula and a selected tree sequence. The results showed acceptable accuracy of D-vine copula in simulating the copula values of the groundwater deficiency signature. After confirming the accuracy of D-vine copula, the probability of occurrence of groundwater deficiency signature was obtained from the joint probability of occurrence of other signatures. This method can be used as a general drought monitoring system for better water resources management in the basin