Groundwater studies in lower part of Ganga-Ramganga interbasin using co-active neuro fuzzy inference system and fuzzy logic
| dc.contributor.advisor | Shiv Kumar | |
| dc.contributor.author | Pradhan, Sucharita | |
| dc.date.accessioned | 2018-09-11T09:11:30Z | |
| dc.date.available | 2018-09-11T09:11:30Z | |
| dc.date.issued | 2015-06 | |
| dc.description.abstract | The present study was undertaken in lower part of Ganga-Ramganga interbasin to investigate groundwater behavior, to prepare groundwater inventory for the assessment of groundwater utilization development stage and to study the comparative performance of Co-active Neuro Fuzzy Inference System and Fuzzy Logic rule based model to predict the seasonal depth to water table. Four groundwater models were developed using net groundwater recharge, net groundwater discharge and previous water table depth as input parameters in which model 1 andmodel 2 were developed using seasonal data and model 3 and model 4 were developed using annual data as input for both pre-monsoon as well as post-monsoon seasons. Neuro Solution 5.0 software with 71 % of total data having two to four Gaussian membership function was used for identification of most efficient network among 5 different CANFIS structure whereas Fuzzy Logic Toolbox with MATLAB R2010a was used to develop Fuzzy Logic rule based models. During the study period of 23 years, two hydrograph stations were on rising water table trend; eight hydrograph stations were neither on rising nor falling water table trend and nineteen hydrograph stations were found to be on falling water table trend during both pre-monsoon and post-monsoon seasons. The water table trend for rest hydrograph stations was not same during pre-monsoon and post-monsoon seasons. Numbers of minor irrigation structures like private tube wells and pump sets on bore wells along with area irrigated by different minor irrigation structures were increasing at an alarming rate. The cropping pattern revealed an increasing trend of area under high water demanding crops like rice and wheat while area under all minor crops except vegetables were found to be decreasing. The groundwater inventory indicated that during the study period, out of 25 blocks of study area, 22 blocks transformed from lower category to higher category of groundwater utilization development stage. The values of performance indicator such as R2, MAD, RMSE, CVRE, CE, r, APE and PI were calculated to evaluate the performance of CANFIS and Fuzzy Logic rule based models. Based on the values of performance indicator for CANFIS models, model 3 with CANFIS-2 structure and model 4 with CANFIS-1 structure were selected for prediction of depth to water table of pre-monsoon and post-monsoon seasons respectively. Further on the basis of values of performance indicator for Fuzzy Logic rule based models, model 3 and model 4 were selected for prediction of depth to water table of pre-monsoon and post-monsoon seasons respectively. By comparing CANFIS and Fuzzy Logic models on qualitative and quantitative basis, Fuzzy Logic rule based models were found to be better than CANFIS models. It was also concluded that, even though the results of CANFIS models were not as accurate as that of Fuzzy Logic rule base models, still CANFIS models confirmed its potential to recognize the trend of depth to water table during the period of study. | en_US |
| dc.identifier.uri | http://krishikosh.egranth.ac.in/handle/1/5810072678 | |
| dc.keywords | groundwater level, rivers, basins, fuzzy logic | en_US |
| dc.language.iso | en | en_US |
| dc.pages | 117 | en_US |
| dc.publisher | G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand) | en_US |
| dc.research.problem | Groundwater Extraction | en_US |
| dc.sub | Soil and Water Engineering | en_US |
| dc.subject | null | en_US |
| dc.theme | Irrigation | en_US |
| dc.these.type | M.Tech | en_US |
| dc.title | Groundwater studies in lower part of Ganga-Ramganga interbasin using co-active neuro fuzzy inference system and fuzzy logic | en_US |
| dc.type | Thesis | en_US |