Evaporation, one of the fundamental components of the hydrology cycle, is differently influenced by various meteorological variables in different climatic regions. The accurate prediction of evaporation is essential for multiple water resources engineering applications, particularly in developing countries like Iraq where the meteorological stations are not sustained and operated appropriately for in situ estimations. This is where advanced methodologies such as machine learning (ML) models can make valuable contributions. In this research, evaporation is predicted at two different meteorological stations located in arid and semi-arid regions of Iraq. Four different ML models for the prediction of evaporation-the classification and regression tree (CART), the cascade correlation neural network (CCNNs), gene expression programming (GEP), and the support vector machine (SVM)were developed and constructed using various input combinations of meteorological variables. The results reveal that the best predictions are achieved by incorporating sunshine hours, wind speed, relative humidity, rainfall, and the minimum, mean, and maximum temperatures. The SVM was found to show the best performance with wind speed, rainfall, and relative humidity as inputs at Station I (R 2 = .92), and with all variables as inputs at Station II (R 2 = .97). All the ML models performed well in predicting evaporation at the investigated locations.

Functional data analysis tools, such as function-on-function regression models, have received considerable attention in various scientific fields because of their observed high-dimensional and complex data structures. Several statistical procedures, including least squares, maximum likelihood, and maximum penalized likelihood, have been proposed to estimate such function-onfunction regression models. However, these estimation techniques produce unstable estimates in the case of degenerate functional data or are computationally intensive. To overcome these issues, we proposed a partial least squares approach to estimate the model parameters in the functionon-function regression model. In the proposed method, the B-spline basis functions are utilized to convert discretely observed data into their functional forms. Generalized cross-validation is used to control the degrees of roughness. The finite-sample performance of the proposed method was evaluated using several Monte-Carlo simulations and an empirical data analysis. The results reveal that the proposed method competes favorably with existing estimation techniques and some other available function-on-function regression models, with significantly shorter computational time.Recent advances in computer storage and data collection have enabled researchers in diverse branches of science such as, for instance, chemometrics, meteorology, medicine, and finance, recording data of characteristics varying over a continuum (time, space, depth, wavelength, etc.). Given the complex nature of such data collection tools, the availability of functional data, in which observations are sampled over a fine grid, has progressively increased. Consequently, the interest in functional data analysis (FDA) tools is significantly increasing over the years. Silverman (2002, 2006), Ferraty and Vieu (2006), Horvath and Kokoszka (2012) and Cuevas (2014) provide excellent overviews of the research on theoretical developments and case studies of FDA tools.Functional regression models in which both the response and predictors consist of curves known as, function-on-function regression, have received considerable attention in the literature. The main goal of these regression models is to explore the associations between the functional response and the functional predictors observed on the same or potentially different domains as the response function. In this context, two key models have been considered: the varying-coefficient model and the function-on-function regression model (FFRM). The varying-coefficient model assumes that the functional response Y (t) and functional predictors X (t) are observed in the same domain. Its estimation and test procedures have been studied by numerous authors, including Fan and Zhang among many others. In contrast, the FFRM considers cases in which the functional response Y (t) for a given continuum t depends on the full trajectory of the predictors X (s). Compared with the varying-coefficient model, the FFRM is more natural; therefore, we restrict our attent...

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Aggregation of large databases in a specific format is a frequently used process to make the data easily manageable. Interval-valued data is one of the data types that is generated by such an aggregation process. Using traditional methods to analyze interval-valued data results in loss of information, and thus, several interval-valued data models have been proposed to gather reliable information from such data types. On the other hand, recent technological developments have led to high dimensional and complex data in many application areas, which may not be analyzed by traditional techniques. Functional data analysis is one of the most commonly used techniques to analyze such complex datasets. While the functional extensions of much traditional statistical techniques are available, the functional form of the interval-valued data has not been studied well. This paper introduces the functional forms of some well-known regression models that take interval-valued data. The proposed methods are based on the function-on-function regression model, where both the response and predictor/s are functional. Through several Monte Carlo simulations and empirical data analysis, the finite sample performance of the proposed methods is evaluated and compared with the state-of-the-art.Due to recent technological advances, the process of collecting data has become complicated, causing high dimensional and complex data structures. Symbolic data analysis is one of the commonly used methods in modeling such complex and large datasets, see Billard (2011) and Noirhomme-Fraiture and Brito (2011) for recent developments in symbolic data analysis. Contrary to single-valued observations in p-dimensional space where classical statistical methods work on, symbolic data may be in the form of hypercubes in p-dimensional space. There are many symbolic data types, for example, list, histogram, modal-valued, and interval-valued data. In this research, we restrict our attention to the interval-valued data only. The data expressed in an interval format (minimum and maximum values of the data) is called the interval-valued data. Such datasets are frequently encountered in daily life, for example, air and/or surface temperature, wind speed, energy production, blood pressure, and exchange rates. The main problem encountered during the modeling of the interval-valued data with classical statistical techniques is "how the variability of observations within the range is involved in modeling?". Traditional methods analyze interval-valued data using its summary (i.e., mid-points), and this approach results in loss of information. Therefore, interval-valued data analysis techniques are needed to obtain more accurate information.The early studies about the interval-valued data regression were conducted by Billard and Diday (2000), who extended traditional statistical techniques to the interval-valued data. Billard and Diday (2002) extended several classical regression models to interval-valued data and they proposed a regression equation for fitting histogram...