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Comparison of Stepwise Multilinear Regressions, Artificial Neural Network, and Genetic Algorithm-Based Neural Network for Prediction the Plant Available Water of Unsaturated Soils in a Semi-arid Region of Iran (Case Study: Chaharmahal Bakhtiari Province)
Communications in Soil Science and Plant Analysis ( IF 1.8 ) Pub Date : 2020-10-05
Reihaneh Soleimani, Elham Chavoshi, Hossein Shirani, Isa Esfandiar Pour

ABSTRACT

Plant available water (PAW) is one of the physical parameters of soils and the basic data of irrigation plans. Although various theoretical or empirical approaches have been proposed to describe this phenomenon, it is still possible to investigate and evaluate the relevance and applicability of new sciences such as artificial neural network method in predicting this phenomenon. In existing methods for determination of PAW, time-consuming tests are required. Nowadays, the capabilities of artificial neural network (ANN) methods in modeling have led to the use of ANN in parallel with the application of conventional approaches in various engineering sciences. In this study, artificial neural networks have been used as a new method to predict the PAW of soils. The study area is Khanimirza plain in Chaharmahal va Bakhtiari province. Soil sampling was performed randomly from 0 to 20 cm depth. The measured property in this study was the amount of plant available water (PAW). Readily available parameters including sand, silt and clay percentage, organic carbon, bulk density (BD), pH, Electrical conductivity (EC), calcium carbonate equivalent (CCE), and calcium carbonate are considered as model inputs. Modeling was performed using Stepwise multilinear regressions (SMLR), artificial neural network (ANN) and genetic algorithm-based neural network (ANN-GA). The results of PAW modeling showed that ANN-GA model with 0.90 coefficient is better than the other two methods. In general, ANN and ANN-GA showed better performance than SMLR. In fact, ANN and ANN-GA do not use a special type of equations and the network can achieve satisfactory results by establishing a proper relationship between input and output data.



中文翻译:

逐步多线性回归,人工神经网络和基于遗传算法的神经网络用于预测伊朗半干旱地区非饱和土壤植物有效水的比较(案例研究:Chaharmahal Bakhtiari Province)

摘要

植物有效水(PAW)是土壤的物理参数之一,也是灌溉计划的基本数据。尽管已经提出了各种理论或经验方法来描述这种现象,但是仍然有可能研究和评估诸如人工神经网络方法之类的新科学在预测这种现象方面的相关性和适用性。在用于确定PAW的现有方法中,需要耗时的测试。如今,人工神经网络(ANN)方法在建模中的功能已导致将ANN与常规方法在各种工程学中的应用并行使用。在这项研究中,人工神经网络已被用作预测土壤PAW的新方法。研究区域是位于Chaharmahal va Bakhtiari省的Khanimirza平原。从0至20 cm深度随机取样土壤。在这项研究中测得的特性是植物有效水量(PAW)。包括沙子,淤泥和粘土百分比,有机碳,堆密度(BD),pH,电导率(EC),碳酸钙当量(CCE)和碳酸钙在内的现成可用参数被视为模型输入。使用逐步多线性回归(SMLR),人工神经网络(ANN)和基于遗传算法的神经网络(ANN-GA)进行建模。PAW建模的结果表明,系数为0.90的ANN-GA模型优于其他两种方法。通常,ANN和ANN-GA的性能要优于SMLR。事实上,

更新日期:2020-10-05
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