Artificial neural network and time series models for predicting soil salt and water content

Zou, Ping; Yang, Jintao; Fu, Jing-Li; Liu, Guangming; Li, Dongshun

doi:10.1016/j.agwat.2010.02.011

Cited by 74 publications

(33 citation statements)

References 24 publications

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“…There have been many studies into the field of neural networks during the past periods but appeared evidently, preliminary from 1980. At present, the applications of neural networks have emerged clearly in several areas, for example, in the field of modeling, classification and prediction [30].…”

Section: Artificial Network Neural Model (Ann)mentioning

confidence: 99%

Hybrid Forecasting Scheme for Financial Time-Series Data using Neural Network and Statistical Methods

Khairalla¹,

XuNing²,

AL-Jallad³

2017

ijacsa

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Abstract-Currently, predicting time series utilizes as interesting research area for temporal mining aspects. Financial Time Series (FTS) delineated as one of the most challenging tasks, due to data characteristics is devoid of linearity, stationary, noisy, high degree of uncertainty and hidden relations. Several singles' models proposed using both statistical and data mining approaches powerless to deal with these issues. The main objective of this study is to propose a hybrid model, using additive and linear regression methods to combine linear and non-linear models. However, three models are investigated, namely, ARIMA, EXP, and ANN. Firstly, those models are feeding by exchange rate data set (SDG-EURO). Then, the arithmetical outcome of each model is examined as benchmark models and set of aforementioned hybrid models in related literature. Results showed the superiority in hybrid model on all other investigated models based on 0.82% MAPE error's measure for accuracy. Based on the results of this study, we can conclude that further experiments desirable to estimate the weights for accurate combination method and more models essential to be surveyed in the areas of series prediction.

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Section: Artificial Network Neural Model (Ann)mentioning

confidence: 99%

Hybrid Forecasting Scheme for Financial Time-Series Data using Neural Network and Statistical Methods

Khairalla¹,

XuNing²,

AL-Jallad³

2017

ijacsa

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“…Machine-learning approaches, such as artificial neural networks (ANN) and support vector machines (SVM), are another group of models that have been applied during past decades for simulating various hydrological processes including soil water dynamics (Jiang and Cotton, 2004;Ahmad and Simonovic, 2005;Elshorbagy and Parasuraman, 2008;Zou et al, 2010;Dai et al, 2011;Asefa et al, 2006;Yu and Liong, 2007;Lin et al, 2009;Liu et al, 2010;Deng et al, 2011). These approaches provide a great prediction capacity and do not require the knowledge of soil physical properties.…”

Section: Introductionmentioning

confidence: 99%

A comparison of numerical and machine-learning modeling of soil water content with limited input data

Karandish

Šimůnek

2016

Journal of Hydrology

133

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Keywords:Machine-learning models ANFIS HYDRUS-2D MLR SVM Soil water content Water saving irrigation a b s t r a c t Soil water content (SWC) is a key factor in optimizing the usage of water resources in agriculture since it provides information to make an accurate estimation of crop water demand. Methods for predicting SWC that have simple data requirements are needed to achieve an optimal irrigation schedule, especially for various water-saving irrigation strategies that are required to resolve both food and water security issues under conditions of water shortages. Thus, a two-year field investigation was carried out to provide a dataset to compare the effectiveness of HYDRUS-2D, a physically-based numerical model, with various machine-learning models, including Multiple Linear Regressions (MLR), Adaptive Neuro-Fuzzy Inference Systems (ANFIS), and Support Vector Machines (SVM), for simulating time series of SWC data under water stress conditions. SWC was monitored using TDRs during the maize growing seasons of 2010 and 2011. Eight combinations of six, simple, independent parameters, including pan evaporation and average air temperature as atmospheric parameters, cumulative growth degree days (cGDD) and crop coefficient (K c ) as crop factors, and water deficit (WD) and irrigation depth (In) as crop stress factors, were adopted for the estimation of SWCs in the machine-learning models. Having Root Mean Square Errors (RMSE) in the range of 0.54-2.07 mm, HYDRUS-2D ranked first for the SWC estimation, while the ANFIS and SVM models with input datasets of cGDD, K c , WD and In ranked next with RMSEs ranging from 1.27 to 1.9 mm and mean bias errors of À0.07 to 0.27 mm, respectively. However, the MLR models did not perform well for SWC forecasting, mainly due to non-linear changes of SWCs under the irrigation process. The results demonstrated that despite requiring only simple input data, the ANFIS and SVM models could be favorably used for SWC predictions under water stress conditions, especially when there is a lack of data. However, process-based numerical models are undoubtedly a better choice for predicting SWCs with lower uncertainties when required data are available, and thus for designing water saving strategies for agriculture and for other environmental applications requiring estimates of SWCs.

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“…It is also important to adjust seedbed preparation and sowing based on water and salt transport under local conditions. Many domestic scholars have studied in detail the spatial distribution and rates of water transport in some typical saline soils in China, including transfer balance in arid areas, especially under irrigation, water and salt movement under irrigation with saline or brackish water (Pang et al, 2010), transport of water and salt under freeze-thaw conditions (Bing and He, 2011), distribution of water and salt under drip irrigation (Min et al, 2014), numerical simulation modeling of water, heat and salt movement in soil (Han et al, 2001), as well as applying genetic algorithm and neural network to the salt and water transport (Zou et al, 2010b). In addition, modern technologies, such as EM31 and EM38 electromagnetic soil conductivity meters, GIS and high precision GPS, have been used widely in monitoring, assessment and early warning of salinization with improved accuracy and efficiency (Fang et al, 2005;.…”

Section: Integrated Technology Of Efficient Agricultural Utilization mentioning

confidence: 99%

Developing and sustainably utilize the coastal mudflat areas in China

Long

Liu

Shao

et al. 2016

Science of The Total Environment

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Artificial neural network and time series models for predicting soil salt and water content

Cited by 74 publications

References 24 publications

Hybrid Forecasting Scheme for Financial Time-Series Data using Neural Network and Statistical Methods

Hybrid Forecasting Scheme for Financial Time-Series Data using Neural Network and Statistical Methods

A comparison of numerical and machine-learning modeling of soil water content with limited input data

Developing and sustainably utilize the coastal mudflat areas in China

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