Recurrent Neural Network Based Forecasting of Crop Production in Nepal
| dc.contributor.author | Joshi, Surendra | |
| dc.date.accessioned | 2022-01-06T07:50:54Z | |
| dc.date.available | 2022-01-06T07:50:54Z | |
| dc.date.issued | 2018-11 | |
| dc.description | In this study, Gated Recurrent Unit (GRU) model was used for predicting Rice crop production in Nepal using climatic and fertilizer variables. | en_US |
| dc.description.abstract | In this study, Gated Recurrent Unit (GRU) model was used for predicting Rice crop production in Nepal using climatic and fertilizer variables. The climatic variables used were Maximum Temperature, Minimum Temperature, Morning Humidity, Evening Humidity and Rainfall and Ecological Regions and fertilizer variables were Nitrogen, Phosphorous, Potassium and Compost. When the model was trained on 70% of data and tested on 30% of the data, the accuracy of the model was 81% for predicting the production. When tested on year 2016, accuracy of the model was 81.33% and for year 2017, the accuracy of the model was 73.33%. While GRU was compared with baseline Artificial Neural Network (ANN) with same architecture for Siraha district, it performed better than baseline ANN. But when input variables were increased, it performed even better. This proved that GRU can be used for optimal prediction of Rice crop in Nepal. | en_US |
| dc.identifier.uri | https://elibrary.tucl.edu.np/handle/20.500.14540/7106 | |
| dc.language.iso | en | en_US |
| dc.publisher | Pulchowk Campus | en_US |
| dc.subject | ANN | en_US |
| dc.subject | Climate | en_US |
| dc.subject | Fertilizer | en_US |
| dc.subject | GRU | en_US |
| dc.title | Recurrent Neural Network Based Forecasting of Crop Production in Nepal | en_US |
| dc.type | Thesis | en_US |
| local.academic.level | Masters | en_US |
| local.affiliatedinstitute.title | Pulchowk Campus | en_US |
| local.institute.title | Institute of Engineering | en_US |