بررسی عملکرد روش های داده مبنا در تخمین نقاط مهم رطوبتی در منطقه شاهرود

آگاهی از نقاط مهم رطوبتی، برای مطالعه های آبیاری در مزرعه بسیار ضروری می باشد اما اندازه گیری این اطلاعات به روش مستقیم بسیار پرهزینه و وقت گیر است. روش های داده مبنا می توانند روش مناسبی برای تخمین این پارامترها باشد. تحقیق حاضر به برآورد نقاط مهم رطوبتی شامل ظرفیت زراعی و نقطه پژمردگی دایم به وسیله پارامترهای زودیافت با سه روش شبکه عصبی، رگرسیون خطی چندمتغیره و رگرسیون بردار پشتیبان در منطقه شاهرود پرداخته است. پس از نرمال سازی داده های مورد نظر جدول ضریب همبستگی متغیرهای ورودی احتمالی با خروجی های مورد نظر تشکیل شد و معنی داری همبستگی متغیرهای ورودی و خروجی از نظر آماری بررسی‌گردید. سپس، مدل سازی با روش های مذکور انجام و نتایج مورد ارزیابی قرار گرفت. نتایج نشان داد که روش رگرسیون بردار پشتیبان کارایی بهتری نسبت به دو روش دیگر  دارد. مقادیر ضریب تعیین، انحراف جذر میانگین مربعات خطا و ریشه میانگین مربعات خطا نرمال شده در بهترین مدل رگرسیون بردار پشتیبان ، به ترتیب برابر 85/0 ، 12/3 و 89/12 برای ظرفیت زراعی و 83/0 ، 58/1و 84/14  برای نقطه پژمردگی دایم و برای شبکه های عصبی مقادیر 72/0 ، 48/3 و 36/14 برای ظرفیت زراعی و 75/0 ، 90/1 و 91/17 برای نقطه پژمردگی به دست آمد. با توجه به بررسی های صورت گرفته در این تحقیق، می توان بیان نمود که مدل های رگرسیون بردار پشتیبان با تابع کرنل خطی پایه شعاعی قادر خواهند بود  با خطا ی پایین و ضریب تعیین بالا  نقاط مهم رطوبتی خاک را پیش بینی کنند و همچنین می توانند جایگزین بسیار خوبی برای روش های سنتی هم چون شبکه های عصبی و رگرسیون خطی باشند.

Investigating the Performance of Data-based Methods in Estimating Important Moisture Points in Shahrood Area

Awareness of the important moisture points is crucial for irrigation studies on the farm, but measuring this information in a direct way is very costly and time consuming. Therefore, several models and relationships have been developed as Pedotransfer functions which indirectly predict the hydrological properties of the soil using readily available soil data with the aid of a series of proper mathematical relationships (Nguyen et al., 2015). Since the measurement of important moisture points is a time consuming, costly and difficult work, many attempts have been made in order to use simpler soil properties such as texture, the amount of organic matter, and bulk density. Pedotransfer functions are indeed predictive functions which establish relationship between the soil’s readily available and latency data (e.g., the percentage of sand, silt and clay, bulk density and organic matter) including the parameters of the moisture curve (field capacity and permanent wilting point) (Botulla et al., 2013). Moreover, the functions that can be successfully implemented in an area may not have suitable adaptations in another area with real values. There are several methods for obtaining Pedotransfer functions, among them  are linear regression (LR), artificial neural networks, fuzzy adaptiveneural inference, and support regression vector.  Various researchers have studied the development of Pedotransfer functions and evaluated the predictive models in the water and soil sciences. As a sample, Shop and Lajj (1998) estimated the soil moisture curve using the neural network. They found that the artificial neural network was better than some of the regression Pedotransfer functions provided by other researchers, and if more readily available properties were used as inputs, the prediction accuracy increased. However, there was always a significant difference between the predicted and measured moisture values. Zhang et al. (2007) estimated the soil moisture curve for 110  noncalcareous soil samples with different tissue classes through the artificial neural networks and regression models. They showed that the neural network predicts the moisture curve better than the regression method with higher correlation coefficient in most tissue classes. Lin et al. (2009) argued that the SVM method was much faster trained than the artificial neural network. SVM was also found to have a more accurate prediction than the artificial neural network. Chen et al. (2010) used support vector machines to model daily rainfall and compared the results with that of the multivariate analysis method. It was found that the results of predictions from SVM were more accurate. In turn, Kaihua et al. (2014) used support vector machines to predict cationic capacity on different horizons of the soil in Qingdao, China. They performed their studies at 208 points on two horizons of the soil, and concluded that the SVM model improved predictions. Considering the significance of knowing the important points of soil moisture in Shahrood area for agricultural projects and irrigation schedules, developing appropriate Pedotransfer functions and evaluating models is necessary so as to obtain moisture of the field capacity and permanent wilting point. This research, thus, evaluates the performance of three models of support vector regression, artificial neural networks, and linear regression in the development of soil Pedotransfer functions and the effect of number and type of input variables on the performance of the models.