Spatio-temporal prediction of crop disease severity for agricultural emergency management based on recurrent neural networks

Xu, Wei; Wang, Qili; Chen, Runyu

doi:10.1007/s10707-017-0314-1

Cited by 35 publications

(15 citation statements)

References 39 publications

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“…N (n) refers to the total number of points, while y i and y i are the predicted values and their corresponding observations. This exact metric was used to evaluate the proposed model's accuracy with other predictive models adapted from Xu et al [51].…”

Section: Results Evaluationmentioning

confidence: 99%

Land Use Change Ontology and Traffic Prediction through Recurrent Neural Networks: A Case Study in Calgary, Canada

Azad

Xin

2021

IJGI

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Land use and transportation planning have a significant impact on the performance of cities’ traffic conditions and the quality of people’s lives. The changing characteristics of land use will affect and challenge how a city is able to manage, organize, and plan for new developments and transportation. These challenges can be better addressed with effective methods of monitoring and predicting, which can enable optimal efficiency in how a growing city like Calgary, Canada, can perform. Using ontology in land use planning is a new initiative currently being researched and explored. In this regard, ontology incorporates relationships between the various entities of land use. The aim of this study is to present Land Use Change Ontology (LUCO) with a deep neural network for traffic prediction. We present a Land Use Change Ontology (LUCO) approach, using expressions of how the semantics of land use changes relate to the integration of temporal land use information. This study examines the City of Calgary’s land use data from the years 2001, 2010, and 2015. In applying the LUCO approach to test data, experimental outcomes indicated that from 2001 to 2015 residential land use increased by 30% and open space decreased by 40%. Forecasting traffic is increasingly essential for successful traffic modelling, operations, and management. However, traditional means for predicting traffic flow have largely assumed restrictive model architectures that have not controlled for the amounts of land use change. Inspired by deep learning methods and effective data mining computing capabilities, this paper introduces the deep learning Recurrent Neural Network (RNN) to predict traffic while considering the impact of land use change. The RNN was successful in learning the features of traffic flow under various land use change situations. Experimental results indicated that, with the consideration of LUCO, the deep learning predictors had better accuracy when compared with other existing models. Success of our modeling approach indicates that cities could apply this modeling approach to make land use transportation planning more efficient.

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Section: Results Evaluationmentioning

confidence: 99%

Land Use Change Ontology and Traffic Prediction through Recurrent Neural Networks: A Case Study in Calgary, Canada

Azad

Xin

2021

IJGI

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“…This study also used the Friedman Test [36] to better understand the performance of each of the models. A comparative study of the methodology was adapted from Xu et al [33] who found the lower value of absolute percentage errors (MAPE) provided better results. As seen in Table 4, the MAPE from each of the modelling forecasts were analyzed.…”

Section: Deep Neural Network Recurrent Neural Network (Dnn-rnn) Resultsmentioning

confidence: 99%

“…To evaluate the performance of the predictor, three performance measurements were selected, which were the mean absolute error N (n) denoted the total number of points, while and were the predicted values and their corresponding observations. This same metric was used to compare the accuracy of the proposed architecture with that obtained using other predictive algorithms adapted from Xu et al [33]. The implementation of the traffic prediction algorithm was done in Python, using Tensorflow as backend.…”

Section: Fitting and Comparing Modelsmentioning

confidence: 99%

Untitled

2020

CiVEJ

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Land use and transportation planning are interdependent , as well as being important factors in forecasting urban development. In recent years, predicting traffic based on land use, along with several other variables, has become a worthwhile area of study. In this paper, it is proposed that Deep Neural Network Regression (DNN-Regression) and Recurrent Neural Network (DNN-RNN) methods could be used to predict traffic. These methods used three key variables: land use, demographic and temporal data. The proposed methods were evaluated with other methods, using datasets collected from the City of Calgary, Canada. The proposed DNN-Regression focused on demographic and land use variables for traffic prediction. The study also predicted traffic temporally in the same geographical area by using DNN-RNN. The DNN-RNN used long short-term memory to predict traffic. Comparative experiments revealed that the proposed DNN-Regression and DNN-RNN models outperformed other methods.

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“…However, GWR is a linear method that cannot consider the nonlinear behaviour of the phenomenon. Owing to high capability in solving nonlinear problems, Artificial Neural Network (ANN), a widely used approach in disease prediction, is selected to predict leptospirosis disease [36–39]. Another approach used in this study is General Linear Model (GLM), which is a statistical model commonly used in modelling and predicting diseases [40].…”

Section: Introductionmentioning

confidence: 99%

Prediction mapping of human leptospirosis using ANN, GWR, SVM and GLM approaches

et al. 2019

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Background: Recent reports of the National Ministry of Health and Treatment of Iran (NMHT) show that Gilan has a higher annual incidence rate of leptospirosis than other provinces across the country. Despite several efforts of the government and NMHT to eradicate leptospirosis, it remains a public health problem in this province. Modelling and Prediction of this disease may play an important role in reduction of the prevalence. Methods: This study aims to model and predict the spatial distribution of leptospirosis utilizing Geographically Weighted Regression (GWR), Generalized Linear Model (GLM), Support Vector Machine (SVM) and Artificial Neural Network (ANN) as capable approaches. Five environmental parameters of precipitation, temperature, humidity, elevation and vegetation are used for modelling and predicting of the disease. Data of 2009 and 2010 are used for training, and 2011 for testing and evaluating the models. Results: Results indicate that utilized approaches in this study can model and predict leptospirosis with high significance level. To evaluate the efficiency of the approaches, MSE (GWR = 0.050, SVM = 0.137, GLM = 0.118 and ANN = 0.137), MAE (0.012, 0.063, 0.052 and 0.063), MRE (0.011, 0.018, 0.017 and 0.018) and R 2 (0.85, 0.80, 0.78 and 0.75) are used. Conclusion: Results indicate the practical usefulness of approaches for spatial modelling and predicting leptospirosis. The efficiency of models is as follow: GWR > SVM > GLM > ANN. In addition, temperature and humidity are investigated as the most influential parameters. Moreover, the suitable habitat of leptospirosis is mostly within the central rural districts of the province.

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Spatio-temporal prediction of crop disease severity for agricultural emergency management based on recurrent neural networks

Cited by 35 publications

References 39 publications

Land Use Change Ontology and Traffic Prediction through Recurrent Neural Networks: A Case Study in Calgary, Canada

Land Use Change Ontology and Traffic Prediction through Recurrent Neural Networks: A Case Study in Calgary, Canada

Untitled

Prediction mapping of human leptospirosis using ANN, GWR, SVM and GLM approaches

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