Soil Moisture Prediction in Peri-urban Beijing, China: Gene Expression Programming Algorithm

Niu, Hongfei; Meng, Fanyu; Yue, H.; Yang, Lihong; Dong, Junchen; Zhang, Xin

doi:10.32604/iasc.2021.010131

Cited by 5 publications

(3 citation statements)

References 19 publications

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“…In recent years, with the continuous occurrence of natural disasters such as La Niña, the global temperature has undergone greater changes than before, and water resources have once again been in short supply, which has led to insufficient irrigation for some agriculture around the world. According to surveys, about 50 % of water resources are wasted in the irrigation process, among which soil moisture is the key factor affecting the agricultural irrigation process [1] , so domestic and foreign scholars use different methods to predict soil moisture, among which Xiaoyu An [2] et al made a prediction of soil moisture by BP neural network under BAS optimization, thus overcoming the shortcomings of long training and slow convergence of BP neural network; Hongfei Niu [3] et al used the GEP algorithm for soil moisture prediction and demonstrated the feasibility of their model from the mean square error; Yu Cai [4] used the DRRN model to predict soil moisture and compared with several other deep learning and machine learning models. Thus, it has been demonstrated that the DRRN model has good accuracy for soil moisture.…”

Section: Figure 1 Geographical Distribution Of Grasslands In Inner Mo...mentioning

confidence: 99%

Prediction of soil moisture in Inner Mongolia’s League based on machine learning

Zhu,

Jing,

Ding

2023

Fourth International Conference on Signal Processing and Computer Science (SPCS 2023)

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Section: Figure 1 Geographical Distribution Of Grasslands In Inner Mo...mentioning

confidence: 99%

Prediction of soil moisture in Inner Mongolia’s League based on machine learning

Zhu,

Jing,

Ding

2023

Fourth International Conference on Signal Processing and Computer Science (SPCS 2023)

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“…[16,17]. Niu et al [18] found that the GEP model based on temperature, barometric pressure, humidity, wind speed, ground temperature, rainfall, and initial temperature values as model inputs could better achieve soil moisture prediction. Fu et al [19] used the ensemble Kalman filter (EnKF) and simple biosphere model (SiB2) for soil moisture assimilation prediction to study the initial state values at different assimilation frequencies and rainfall on soil moisture prediction and found that soil moisture prediction was influenced by precipitation during the prediction period.…”

Section: Introductionmentioning

confidence: 99%

BAG: A Linear-Nonlinear Hybrid Time Series Prediction Model for Soil Moisture

Wang

Zhuang

et al. 2023

Agriculture

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Soil moisture time series data are usually nonlinear in nature and are influenced by multiple environmental factors. The traditional autoregressive integrated moving average (ARIMA) method has high prediction accuracy but is only suitable for linear problems and only predicts data with a single column of time series. The gated recurrent unit neural network (GRU) can achieve the prediction of time series and nonlinear multivariate data, but a single nonlinear model does not yield optimal results. Therefore, a hybrid time series prediction model, BAG, combining linear and nonlinear characteristics of soil moisture, is proposed in this paper to achieve the identification process of linear and nonlinear relationships in soil moisture data so as to improve the accuracy of prediction results. In BAG, block Hankel tensor ARIMA (BHT-ARIMA) and GRU are selected to extract the linear and nonlinear features of soil moisture data, respectively. BHT-ARIMA is applied to predict the linear part of the soil moisture, and GRU is used to predict the residual series, which is the nonlinear part, and the superposition of the two predicted results is the final prediction result. The performance of the proposed model on five real datasets was evaluated. The results of the experiments show that BAG has a higher prediction accuracy compared with other prediction models for different amounts of data and different numbers of environmental factors.

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“…Different machine learning-based models have been developed with varying degrees of success for soil moisture estimation using a variety of environmental inputs (such as air and soil temperature, relative humidity, etc. ), with some requiring initial soil moisture measurements as inputs [ 25 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 ]. In addition, it has been pointed out that the Relative Signal Strength Indicator (RSSI) parameter, which quantifies signal attenuation through soil, is an easily measurable variable for any WUSN and can be a powerful input for soil moisture measurement.…”

Section: Introductionmentioning

confidence: 99%

A Wireless Underground Sensor Network Field Pilot for Agriculture and Ecology: Soil Moisture Mapping Using Signal Attenuation

Balivada

Grant

Zhang

et al. 2022

Sensors

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Wireless Underground Sensor Networks (WUSNs) that collect geospatial in situ sensor data are a backbone of internet-of-things (IoT) applications for agriculture and terrestrial ecology. In this paper, we first show how WUSNs can operate reliably under field conditions year-round and at the same time be used for determining and mapping soil conditions from the buried sensor nodes. We demonstrate the design and deployment of a 23-node WUSN installed at an agricultural field site that covers an area with a 530 m radius. The WUSN has continuously operated since September 2019, enabling real-time monitoring of soil volumetric water content (VWC), soil temperature (ST), and soil electrical conductivity. Secondly, we present data collected over a nine-month period across three seasons. We evaluate the performance of a deep learning algorithm in predicting soil VWC using various combinations of the received signal strength (RSSI) from each buried wireless node, above-ground pathloss, the distance between wireless node and receive antenna (D), ST, air temperature (AT), relative humidity (RH), and precipitation as input parameters to the model. The AT, RH, and precipitation were obtained from a nearby weather station. We find that a model with RSSI, D, AT, ST, and RH as inputs was able to predict soil VWC with an R2 of 0.82 for test datasets, with a Root Mean Square Error of ±0.012 (m3/m3). Hence, a combination of deep learning and other easily available soil and climatic parameters can be a viable candidate for replacing expensive soil VWC sensors in WUSNs.

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Soil Moisture Prediction in Peri-urban Beijing, China: Gene Expression Programming Algorithm

Cited by 5 publications

References 19 publications

Prediction of soil moisture in Inner Mongolia’s League based on machine learning

Prediction of soil moisture in Inner Mongolia’s League based on machine learning

BAG: A Linear-Nonlinear Hybrid Time Series Prediction Model for Soil Moisture

A Wireless Underground Sensor Network Field Pilot for Agriculture and Ecology: Soil Moisture Mapping Using Signal Attenuation

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