ghahavand plain

The deficiency of surface water in arid and semi-arid territories has exacerbated the dependence on groundwater resources, resulting in considerable reductions in groundwater levels. This phenomenon has been particularly pronounced in numerous plains throughout Iran, where the diminution has exacerbated issues related to land subsidence. A comprehensive understanding of groundwater level variations is imperative for enhancing water management strategies and alleviating the associated hazards. A range of statistical, mathematical, and machine-learning methodologies have been utilized to model the dynamics of groundwater aquifers. Recently, deep neural network algorithms have gained prominence in the investigation of surface and groundwater resources, particularly in light of the spatiotemporal characteristics inherent to groundwater. In the present investigation, a hybrid spatiotemporal data mining framework, denoted as Wavelet-PCA, was employed to analyze data acquired from 44 piezometric wells situated in the Qahavand plain over a span of three decades (1988-2018) for the purpose of elucidating temporal and spatial patterns associated with fluctuations in groundwater levels. Subsequently, a sophisticated deep recurrent neural network architecture incorporating Long Short-Term Memory (LSTM) was implemented to model the time series data resulting from the data mining procedure. Various degrees of wavelet transformation were applied to effectively capture the intricate trends in groundwater levels. The LSTM model exhibited a coefficient of determination (R²) of 0.85 for the training dataset while achieving an R² of 0.62 for the testing dataset. The research additionally examined regional patterns of land subsidence utilizing radar interferometry data obtained from the Sentinel-1 satellite during the period from 2014 to 2019. The results revealed an average maximum subsidence measurement of 9 centimeters, with the most pronounced subsidence noted in regions that are undergoing the most substantial declines in groundwater levels. This observed relationship between groundwater depletion and land subsidence underscores the necessity for judicious land use planning and the implementation of effective water resource management strategies in analogous regions.

Subsidence is a danger that threatens many plains of Iran. Qahavand plain in Hamadan province is one of the plains that is exposed to this danger due to the unfavorable hydro-climatic conditions and the type of human activities. Considering the importance of the subject, in this research, the subsidence condition of the Ghahavand plain and the factors influencing its occurrence have been investigated. According to the subject and objectives, in this research, 31 radar images of Sentinel 1 satellite, Landsat 8 satellite image and 30 meters high digital model were used as research data. The most important research tools are GMT, ENVI and ArcGIS software. This research has been done in three stages, in the first stage, the land cover map was prepared and examined. In the second stage, using the SBAS time series method, the subsidence situation of the region was evaluated and in the third stage, the effective factors in the subsidence of the region were analyzed. According to the results of this research, the Ghahavand Plain has subsided between 15 and 78 mm during a 2-year period (from 2017/01/05 to 2019/01/19). Also, the results of this research have shown that the main cause of subsidence in the region was the sharp drop in underground water resources due to the development of irrigated agricultural lands. In fact, the highest amount of subsidence in the region has corresponded to irrigated agricultural lands

Evaluation of groundwater quality is important for management of agricultural industry. Aim of this study is the evaluation of groundwater quality in Razan-Ghahavand plain by GIS for preparation of water requirement for agriculture. For this purpose we used averages collected data from 49 wells during 4 years period. These data include Electrical Conductivity, Total Dissolved Solids, pH, Chloride (Cl), Sodium (Na) and Sodium Attraction ratios. At first, the plan of water quality parameters was drawn by the use of descriptive methods prescribed by the consultants of California University and also interpolation methods. Then these plans were integrated together and the study area was divided into three zones with good, moderate and poor groundwater qualities for agriculture. Also results show that 43.7% of total area had undesirable concentration of sodium ions.