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The potential of several predictive models including multiple model-artificial neural network (MM-ANN), multivariate adaptive regression spline (MARS), support vector machine (SVM), multi-gene genetic programming (MGGP), and 'M5Tree' were assessed to simulate the pan evaporation in monthly scale (EP m ) at two stations (e.g. Ranichauri and Pantnagar) in India. Monthly climatological information were used for simulating the pan evaporation. The utmost effective input-variables for the MM-ANN, MGGP, MARS, SVM, and M5Tree were determined using the Gamma test (GT). The predictive models were compared to each other using several statistical criteria (e.g. mean absolute percentage error (MAPE), Willmott's Index of agreement (WI), root mean squared error (RMSE), Nash-Sutcliffe efficiency (NSE), and Legate and McCabe's Index (LM)) and visual inspection. The results showed that the MM-ANN-1 and MGGP-1 models (NSE, WI, LM, RMSE, MAPE are 0.954, 0.988, 0.801, 0.536 mm/month, 9.988% at Pantnagar station, and 0.911, 0.975, 0.724, and 0.364 mm/month, 12.297% at Ranichauri station, respectively) with input variables equal to six were more successful than the other techniques during testing period to simulate the monthly pan evaporation at both Ranichauri and Pantnagar stations. Thus, the results of proposed MM-ANN-1 and MGGP-1 models will help to the local stakeholders in terms of water resources management. ARTICLE HISTORY

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Drought tolerance is not often considered an independent trait by plant breeders because the collective result of many traits of a plant with positive or negative interactions may be fairly general and polygenic in nature. The objective of this study was to evaluate six drought tolerance indices, namely the stress susceptibility index (SSI), yield stability index (YSI), tolerance index (TOL), mean productivity (MP), geometric mean productivity (GMP), and stress tolerance index (STI), to be used in screening safflower (Carthamus tinctorius L.) genotypes. For this purpose, 64 genotypes were grown during two growing seasons (2011)(2012) under both normal and drought-stress field conditions at the Research Farm of Isfahan University of Technology located at Lavark, Najaf-Abad, Iran. The drought tolerance indices were calculated based on seed yield under drought stress and nonstress conditions. Results of combined analysis of variance showed the significant influences of drought stress on seed yield as well as significant differences among genotypes for seed yield and the indices. Results of calculated correlation coefficients and multivariate analyses showed that GMP and STI indices were able to discriminate between drought-sensitive and -tolerant safflower genotypes. Cluster analysis using the drought-tolerance indices divided the 64 genotypes into tolerant and susceptible groups. Based on multivariate analyses using the indices singly or in combinations, it was possible to identify the most yield-stable genotypes across the environments. Overall, we concluded that GMP and STI indices can be efficiently exploited not only to screen drought tolerance but also to identify superior genotypes for both stress and nonstress field conditions in safflower.

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Lakes are primitive water holding geographic structures containing most the fresh water on the Earth's surface, but the recent trends show that climate change can potentially lead to a significant aberration in the Lake water level and its overall pristine state, and therefore, could also threaten the source of freshwater. The ability to forecast the lake water is a paramount decision-making and risk-reduction task, and this is required to retain the sustainability of the natural environment, and to reduce the risk to the local and global food chain, recreation activities, agriculture and ecosystems. In this study, we have designed and evaluated a new hybrid forecasting model, integrating the gravitational search algorithm (GSA), as a heuristic optimization tool, with the Multilayer Perceptron (MLP-GSA) algorithm to forecast water level in Winnipesaukee and Cypress Lakes in the United States of America. The performance of the resulting hybrid MLP-GSA model is benchmarked and compared with the traditional MLP trained with Levenberg-Marquadt back propagation learning algorithm, two other intelligent hybrid models (MLP-PSO and MLP-FFA) and also two stochastic models namely, ARMA and ARIMA models. In this case study, the monthly time scale water level data from 1938 to 2005 and 1942 to 2011 for the Lakes Winnipesaukee and Cypress, respectively, were applied to train and evaluate the MLP-GSA model. The best input combinations of the standalone (MLP) and the hybrid MLP-GSA forecasting models were determined by sensitivity analysis of historical water level training data for 1-month lead forecasting. The hybrid MLP-GSA model was evaluated independently with statistical score metrics: coefficient of correlation, coefficient of efficiency, the root mean square and relative root mean square errors, and the Bayesian Information Criterion. The results showed that the hybrid MLP-GSA4 and MLP-GSA5 model (where the '4 and 5 months' of lagged input combinations of Lake water level data were utilized as the model inputs) performed more accurately than the ARIMA, ARMA, MLP4, MLP-PSO4 and MLP-FFA4 models for the Cypress Lake and ARIMA, ARMA, MLP5, MLP-PSO5 and MLP-FFA5 models for the Winnipesaukee lake, respectively. This study ascertained the robustness of hybrid MLP-GSA over ARMA, ARIMA, MLP, MLP-PSO and MLP-FFA for the forecasting of Lake water level. The high efficacy of the hybrid MLP-GSA model over the other applied models, indicate significant implications of its use in water resources management, decision-making tasks, irrigation management, management of hydrologic structures and sustainable use of water for agriculture and other necessities.

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