Evaluation of data mining techniques to forecast floods in the Bagmati River Basin, Nepal | |
| Author | Maskay, Shristi |
| Call Number | AIT RSPR no.IM-15-03 |
| Subject(s) | Data mining--Evaluation--Bagmati River Basin (Nepal) Flood forecasting--Bagmati--River Basin (Nepal) |
| Note | A research submitted in partial fulfillment of the requirements for the degree of Master of Science in Information Management, School of Engineering and Technology |
| Publisher | Asian Institute of Technology |
| Series Statement | Research studies project report ; no. IM-15-03 |
| Abstract | Nepal witnesses a heavy loss of life and property, every year due to massive floods, landslides and avalanches. This has a direct impact on the overall development of the country. Therefore, to minimize the casualties and damage it is important to focus on implementing techniques for the prediction of extreme environmental and climatic conditions. Thus, this study has been developed with the aim to try to predict such hazards. The study area taken is Bagmati river basin located in Central Nepal and the main objective is to predict flooding. The main objective is the use of different data mining tools and techniques; in particular, multiple linear regression, support vector regression, and artificial neural network with feedforward back propagation to find the correlation between the rainfall and the discharge and t o predict the flood in the Bagmati River Basin. Different d ata mining tools were ap plied to the discharge and precipitation data to predict the discharge at the outlet station. A trial - and - error process was used to select model parameters and to select the best model for prediction. Evaluation methods, in particular, root mean square err or, coefficient of determination and Nash - Sutcliffe model efficiency coefficient analysis were used to determine the accuracy of the models. The result obtained from the evaluation method and the various predicted hydrograph of each model proved ANN model to be better in prediction of discharge though ANN model is considered to be one of the most time consuming models for prediction. The training of model even the testing highly depends on precision of the sample data selected. The outcome depends on the c orrelation of the attributes being used in the dataset and the analysis done on basis of historical records of rainfall and discharge of the various selected hydrological meteorological station of Bagmati river basin. |
| Year | 2015 |
| Corresponding Series Added Entry | Asian Institute of Technology. Research studies project report ; no. IM-15-03 |
| Type | Research Study Project Report (RSPR) |
| School | School of Engineering and Technology (SET) |
| Department | Department of Information and Communications Technologies (DICT) |
| Academic Program/FoS | Information Management (IM) |
| Chairperson(s) | Guha, Sumanta; |
| Examination Committee(s) | Vatcharaporn Esichaikul;Shrestha, Sangam; |
| Scholarship Donor(s) | Asian Institute of Technology Fellowship; |
| Degree | Research Studies Project Report (M. Sc.) - Asian Institute of Technology, 2015 |