Loading...
Please wait, while we are loading the content...
Similar Documents
Drought Monitoring and Prediction using K-Nearest Neighbor Algorithm
| Content Provider | Semantic Scholar |
|---|---|
| Author | Fadaei-Kermani, E. Barani, Ghasem Ghaeini-Hessaroeyeh, Mahnaz |
| Copyright Year | 2017 |
| Abstract | Drought is a climate phenomenon that might occur in any climate condition and all regions on the earth. An effective drought management depends on the application of appropriate drought indices. Drought indices are variables that are used to detect and characterize drought conditions. In this work, it is tried to predict drought occurrence based on the standard precipitation index (SPI) using k-nearest neighbor modeling. The model is tested using the precipitation data of Kerman, Iran. The results obtained show that the model gives reasonable predictions of the drought situation in the region. Finally, the efficiency and precision of the model is quantified by some statistical coefficients. Appropriate values for the correlation coefficient (r = 0.874), mean absolute error (MAE = 0.106), root mean square error (RMSE = 0.119) and coefficient of residual mass (CRM = 0.0011) indicate that the presented model is suitable and efficient. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://jad.shahroodut.ac.ir/article_881_08358d7f133282eb239ac899569e7ebf.pdf |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Approximation error Circumferential Resection Margin Coefficient Droughts K-nearest neighbors algorithm MAV protocol Mean squared error Pearson's marrow-pancreas syndrome Root Mean Square Single Linkage Cluster Analysis Water Resources |
| Content Type | Text |
| Resource Type | Article |
