Abstract

Though groundwater is a replenishable resource, it’s over exploitation has posed greater problem of its depletion. Hence, monitoring and forecasting of groundwater levels has become a primary task of governmental water boards/agencies for sustainable water management. The current study focused on evaluating the performance of Gradient Tree Boosting (GTB) model with that of conventional Adaptive Neuro-Fuzzy Inference System (ANFIS) and Group Method of Data Handling (GMDH) models in forecasting groundwater levels of two coastal aquifers. Data of two groundwater level monitoring wells penetrating into unconfined aquifers located at Shirtadi and Rayee near to Mangalore city of Karnataka state, India was considered in the present study. Monthly groundwater level data of the years 2000 – 2013 were used for model simulation; wherein 70% of data was used for model training and the remaining 30% served as testing data. Comparative result evaluation shows that the proposed GTB approach for one month ahead groundwater level forecasting was giving much accurate results than the other models for the same period of time and same set of data. For Rayee monitoring well, the error statistic, RRMSE of GTB, GMDH and ANFIS models obtained during test phase were 0.473, 0.517 and 0.7522, respectively. The comparison is examined further with different performance metrics.

Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.