evaluation of bayesian networks model in monthly groundwater level prediction (case study: birjand aquifer)
نویسندگان
چکیده
the planning of water resources is based on the volume of water extracted from the aquifer and accurate estimate of this volume considerably helps to development. in this study, the bayesian networks model using continues and clustering structures was used to simulate the groundwater level of birjand aquifer. bayesian networks was calibrated with five input variables of aquifer recharge, water table, temperature, evaporation as well as groundwater withdrawals in the previous month and the groundwater level in the current month as output variable. in continues and clustering scenarios, analysis and calibration of input data is performed based on continuity and uncertainty of variables and some validation indexes respectively and then groundwater level was simulated. the final results showed that the bayesian network is a powerful tool for simulation of groundwater level under uncertainty and average correlation coefficient in 13 piezometers is 0.83 and 0.56 for continues and clustering structures, respectively. also it shows that continues structure can be applied to predict the groundwater level with higher correlation.
منابع مشابه
Performance evaluation of artificial neural networks in statistical downscaling of monthly precipitation (Case study: Minab watershed)
متن کامل
monthly rainfall prediction using artificial neural networks and m5 model tree (case study: station of ahar)
introduction rainfall is considered as one of the most important factures in water cycle. prediction of monthly rainfall is important for many purposes such as estimating torrent, drought, run-off, sediment, irrigation programming and also management of drainage basins. rainfall prediction in each area is mediated by punctual data measured as humidity, temperature, wind speed and etc. as iran i...
متن کاملEvaluation and Simulation of Groundwater Flow in Aquifers Enclosed With Desert Saline Areas (Case Study: Isfahan Province-Ardestan Aquifer)
Quantitative changes in groundwater and crises resulting from uncontrolled water extraction have turned water resources management into one of the supply-demand dilemmas in arid regions. The present study evaluated the quantitative situation of water resources in the Ardestan Plain adjoining the Ardestan desert by using the MODFLOW mathematical model. Simulation of groundwater flow in the stead...
متن کاملa study on insurer solvency by panel data model: the case of iranian insurance market
the aim of this thesis is an approach for assessing insurer’s solvency for iranian insurance companies. we use of economic data with both time series and cross-sectional variation, thus by using the panel data model will survey the insurer solvency.
Detecting the Impact of Climate Change Droughts on Changes in Groundwater Resources. Case Study: Birjand County
Increasing atmospheric anomalies have altered some of the extreme events such as global warming droughts, many climatic components such as precipitation, evapotranspiration, temporal and spatial distribution of precipitation, followed by dominoes of changes in freshwater resources available to communities. Human changes such as changes in the hydrological regime of rivers, changes in the qualit...
متن کاملPrediction of aquifer reaction to different hydrological and management scenarios using visual MODFLOW model-Case study of Qazvin plain
Regarding to increased use of groundwater resources, it seems necessary to have more accurate knowledge of characteristics of aquifers in order to improved usage and accurate management of these resources. In this study, we simulated groundwater flow of Qazvin aquifer usingVisual MODFLOW model, collecting data and statistics, as well as using different studies. The simulation model has operated...
متن کاملمنابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
مدیریت آب و آبیاریجلد ۵، شماره ۲، صفحات ۱۳۹-۱۵۱
کلمات کلیدی
میزبانی شده توسط پلتفرم ابری doprax.com
copyright © 2015-2023