Artificial neural network model to predict transport parameters of reactive solutes from basic soil properties

Mojid, M. A.; Hossain, A.B.M. Zahid; Ashraf, M. Arif

doi:10.1016/j.envpol.2019.113355

Cited by 31 publications

(4 citation statements)

References 32 publications

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“…Ghosal et al established a relatively complete sustainable development indicator system and used the multiobjective linear weighting function model to conduct a systematic comprehensive evaluation of the regional sustainable development process for the frst time [19]. Mojid et al used the concept of resource carrying capacity to improve the national sustainable development evaluation index system, used fve indicators such as resource carrying capacity to determine the regional sustainable development capacity, and used indicators such as resource abundance to determine the sustainable development status [20].…”

Section: Related Workmentioning

confidence: 99%

A New Model of Environmental-Economic Coordination Prediction Using Credible Neural Network Integration and Big Data Analysis

Yang

et al. 2022

Security and Communication Networks

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Currently, a significant portion of sustained economic growth still depends on a high input of resources. We must fully understand the significance, difficulty, and long-term nature of resource conservation and environmental protection. We must also intensify our efforts to protect the ecological environment. We must gradually form a production mode, lifestyle, and consumption mode conducive to environmental protection. We must also establish a benign interactive relationship with the environment. This study offers a big data analysis and neural network integration optimization design strategy for a new kind of environmental and economic coordination prediction model. The data of the new type of environmental and economic impact with numerous parameters are preprocessed using big data analysis and principal component analysis. A neural network integration system is used to create the prediction model, and prediction research and error analysis are conducted to enhance the new kind of environmental and economic model. The simulation test analysis is completed lastly. According to the simulation findings, the proposed arithmetic has an accuracy that is 8.56% higher than that of the conventional arithmetic. A sustainable improvement management system with environmental objectives, environmental management planning plans, and environmental monitoring systems can be established with help from the new environmental and economic coordination prediction model, which can also assist in predicting the potential environmental impact caused by economic development activities. This will ensure sustainable development as a result of the mechanism.

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Section: Related Workmentioning

confidence: 99%

A New Model of Environmental-Economic Coordination Prediction Using Credible Neural Network Integration and Big Data Analysis

Yang

et al. 2022

Security and Communication Networks

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“…The neural network layer contains many neurons, and each neuron is related to each other through weighting, forming an interconnected neural network structure. The most basic ANN consists of an input layer, a hidden layer, and an output layer [28]. The functional characteristics of each layer are shown in Table 2…”

Section: Non-convexitymentioning

confidence: 99%

The Direction Analysis on Trajectory of Fast Neural Network Learning Robot

2021

IEEE Access

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This study is to efficiently apply artificial neural network (ANN) to the robotics, so as to provide experimental basis for mobile robots to learn the optimal trajectory planning strategy. An algorithm model is innovatively proposed based on back propagation neural network (BPNN) and reinforcement learning (Q-Learning) by combining the motion space, selective strategy, and reward function design. The simulation experiment environment is set and the ROS mobile robot is adopted for simulation experiments. The algorithm proposed in this study is compared with other neural network algorithms from the perspectives of accuracy, precision, recall, and F1. It can be found that the accuracy of algorithm proposed was at least 5.47% higher than that of the model algorithm proposed by other scholars, and the values of precision, recall, and F1 were at least 5.5% higher. The results show that the mobile robot could find the shortest trajectory and the best trajectory in a discrete obstacle environment, no matter the more or less the discrete obstacles or the large or small the space. Therefore, compared to the advanced model algorithms proposed by other scholars in related fields, the robot trajectory planning based on the improved BPNN combined with Q-Learning constructed in this study could realize better results, and can be used in practical applications with robot trajectory planning, providing practical value for the field of machine vision.

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“…Recently various stochastic models have been developed and applied in water resources and hydrology, including the soil water simulations by Back Propagation Artificial Neural Network (BP-ANN), Auto Regressive Integrated Moving Average (ARIMA), and Least Squares Support Vector Machine (LS-SVM) (Aitkenhead & Coull 2016;Mojid et al 2019;Asquith 2020). The BP-ANN, ARIMA, and LS-SVM techniques are widely used in water quality prediction, estimation of water demand growth for various purposes (Parmar & Bhardwaj 2015;Zounemat-Kermani et al 2016;Tiyasha et al 2020).…”

Section: Introductionmentioning

confidence: 99%

Simulating and predicting soil water dynamics using three models for the Taihu Lake region of China

Chen

Tang

2022

Water Supply

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Drought stress under a changing climate can significantly affect agricultural production. Simulation of soil water dynamics in field conditions becomes necessary to understand changes of soil water conditions to develop irrigation guidelines. In this study, three models including Auto Regressive Integrated Moving Average (ARIMA), Back Propagation Artificial Neural Network (BP-ANN), and Least Squares Support Vector Machine (LS-SVM) were used to simulate the soil water content in the 0–14 cm and 14–33 cm soil layers across the Taihu Lake region of China. Rainfall, evaporation, temperature, humidity and wind speed that affect soil water content changes were considered in the BP-ANN and LS-SVM, but not in ARIMA. The results showed that the variability of soil water content in 0–14 cm soil layer was greater than that in 14–33 cm. Correlation coefficients (r) of soil water content between simulations and observations were highest (0.9827) using LS-SVM in the 14–33 cm soil layer, while the lowest (0.7019) using ARIMA in the 0–14 cm soil layer; but no significant difference in r values was observed between the two soil layers with the BP-ANN model. Compared to other two models, the LS-SVM model seems to be more accurate for forecasting the dynamics of soil moisture. The results suggested that agro-climatic data can be used to predict the severity of soil drought stress and provide guidance for irrigation to increase crop production in the Taihu Lake region of China.

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Artificial neural network model to predict transport parameters of reactive solutes from basic soil properties

Cited by 31 publications

References 32 publications

A New Model of Environmental-Economic Coordination Prediction Using Credible Neural Network Integration and Big Data Analysis

A New Model of Environmental-Economic Coordination Prediction Using Credible Neural Network Integration and Big Data Analysis

The Direction Analysis on Trajectory of Fast Neural Network Learning Robot

Simulating and predicting soil water dynamics using three models for the Taihu Lake region of China

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