Estimation groundwater depth using ANN-PSO, kriging, and IDW models (case study: Salman Farsi Sugarcane Plantation)
Publish place: Central Asian Journal of Environmental Science and Technology Innovation، Vol: 2، Issue: 3
Publish Year: 1400
نوع سند: مقاله ژورنالی
زبان: English
View: 248
This Paper With 11 Page And PDF Format Ready To Download
- Certificate
- من نویسنده این مقاله هستم
استخراج به نرم افزارهای پژوهشی:
شناسه ملی سند علمی:
JR_CAS-2-3_001
تاریخ نمایه سازی: 16 مرداد 1400
Abstract:
Appropriate management of groundwater resources requires accurate information about the characteristics of the groundwater table, spatial distribution of its characteristics, and the constant depth of the water table and its fluctuations.One of the most important issues in the quantitative management of groundwater resources is the estimation of water table using the data collected from the observation well network.In this study, to simulate the depth of groundwater Salman Farsi Sugarcane Plantation, three methods of Artificial neural network-integrated with particle swarm optimization algorithm, geostatistics (Kriging) and IDW was used. Inputs data include evapotranspiration, air temperature, precipitation and geographic location. The results showed that the highest simulation accuracy of groundwater depth in Salman Farsi Sugarcane Plantation was related to the ANN-PSO model with the highest R۲ (۰.۹۵) index and lowest RMSE and MAE (to ۱.۰۵ and ۱.۱۱) values. Also, among the Kriging and IDW models used, the accuracy of the Kriging model was more than the IDW model. Due to the acceptable accuracy of the results of the three models, the water resource planner and -maker in this field can apply this optimum interpolated groundwater depth to monitor the spatiotemporal fluctuation of groundwater depth in this area by updating its data.
Keywords:
Authors
Atefeh Sayadi Shahraki
Department of Irrigation and Drainage. Shahid Chamran University of Ahvaz, Ahvaz, Iran
Saeed Boroomand Nasab
Department of Irrigation and Drainage. Shahid Chamran University of Ahvaz, Ahvaz, Iran
Abd Ali Naseri
Department of Irrigation and Drainage. Shahid Chamran University of Ahvaz, Ahvaz, Iran
Amir Soltani Mohammadi
Department of Irrigation and Drainage. Shahid Chamran University of Ahvaz, Ahvaz, Iran
مراجع و منابع این Paper:
لیست زیر مراجع و منابع استفاده شده در این Paper را نمایش می دهد. این مراجع به صورت کاملا ماشینی و بر اساس هوش مصنوعی استخراج شده اند و لذا ممکن است دارای اشکالاتی باشند که به مرور زمان دقت استخراج این محتوا افزایش می یابد. مراجعی که مقالات مربوط به آنها در سیویلیکا نمایه شده و پیدا شده اند، به خود Paper لینک شده اند :