Investigating the Effects of Climate Change on Underground Water Sources (Case Study: Khorram Abad plain)

The industrialization of societies has led to a rise in the emission of greenhouse gases in recent years. Such a rise causes global warming and, in turn, affects other components of the climate system, leading to certain climatic events that are fundamentally different from the natural course of climatic events, called climate change . According to the IPCC in 2007, the process of climate change and global warming have a significant impact on various systems such as water resources, agriculture, drinking, and industry such that the continuous increase in greenhouse gas emissions will intensify these effects, causing a warmer climate. Climate change represents the changes in precipitation pattern, snowmelt, and access to drinking water and agriculture . Groundwater resources are susceptible to both climate changes through (A) direct interaction with surface water sources such as rivers and lakes and (B) indirect interaction via the feeding process . Climate change indirectly affects the discharge and storage of groundwater by changing the nutritional conditions induced by rainfall and runoff; therefore, identifying and analyzing effective parameters such as climatic parameters can greatly help predict serious hazards threatening groundwater resources such as subsidence and drought. Moreover, considering that the relationship between climatic parameters and groundwater resources is complex and non-linear, the application of artificial intelligence models including modern hybrid models is a good solution to solving these problems. Therefore, the objective of this study is to analyze and predict climatic parameters using general atmospheric circulation models in coming years and groundwater level forecasting in Khorramabad plain using integrated vector regression model with the help of Wavelet Transform (WT) and modern optimization algorithms such as creative rifle and grey wolf optimizer. The basis of climatic parameters is the groundwater level and abstraction from the aquifer.

it is necessary to provide a solution and make proper forecasting of groundwater resources in order to prevent subsidence and drought phenomena around the world and in Iran. Therefore, in Iran, Khorramabad plain located in Lorestan province, which is very important in terms of drinking and agriculture whose products in this plain feed on groundwater for growth and development, has been subjected to illegal harvesting and digging of illegal wells. The level of groundwater resources has declined sharply in recent years. Therefore, groundwater level changes are more than necessary for forecasting and management measures to improve it.In this study, in order to simulate and predict the groundwater level of Khorramabad plain, the climatic parameters affecting the groundwater level were studied first according to the general circulation models of the atmosphere. In addition, the support vector regression model approach was used to predict the surface water level, assuming that the amount of groundwater abstraction from the Khorramabad plain was consistent with that in the previous statistical period. Since the SVR model is subject to errors according to recent findings, the strategy of optimizing the adjustment parameters using meta-heuristic algorithms was adopted to reduce the model error. To facilitate the groundwater level simulation, new meta-heuristic algorithms with WSVR support vector wave regression model, having acceptable performance according to several studies, were used. This approach can ensure taking an effective step in simulating and predicting groundwater levels.Based on the structure of the SVR, the most basic step is to determine the tuning parameters. The coefficients of these parameters are usually determined through trial and error in SVR. Many factors affect the viability of trial and error and the accuracy ofmodel prediction. Given the nature of trial and error, the predictive power may be generally reduced. Numerous solutions have been proposed by various researchers to address this fundamental weakness. One of these solutions adopted by researchers is to calculate the coefficients of parameter adjustment and optimize these coefficients by using meta-heuristic algorithms. A meta-initiative is the general framework of an algorithm that can provide solutions to the same problem with minor variations of various problems. There are many meta-heuristic algorithms such as Genetic Algorithm, Forbidden Search Simulation, Ant Society, Particle Swarm, Differential Evolution, Harmony Search, Artificial Bee Society, Firefly, Cuckoo or Coco, Frog Leap, Invasive Weed, and Competition, in addition to spirals, pollen, gray wolves, social spiders, ant lions, whales, locusts, and so on. Therefore, to optimize the adjustment coefficients in SVR, this study has employed new optimization algorithms including creative rifle and black widow spider, presented in 2021, for the first time in hydrology and water issues.

In this study, upon using climate change modeling, meteorological parameters (temperature and precipitation) for the years 2021-2040 were predicted and, then, by using ultra-exploratory hybrid models such as WSVR, AIG-SVR, and BWO-SVR, the groundwater level decline in Khorramabad plain located in Iran was predicted with the help of rainfall, temperature and harvest parameters associated with the four piezometric aquifers (Sarab Pardeh, Sali, Pol baba, and Naservand). Evaluation of LARS-WG model using baseline data (1990-2014) demonstrated that this model had a good performance in predicting meteorological parameters. The simulated temperature in all of the climatic models (EC-EARTH, GFDL-CM3, HADGEM2, MIROC4, and MPI-ESM) under the RCP85 emission scenario in the future time period (2021-2040) experienced an increase, compared to the base period in all months, while the average rainfall did not follow a definite trend. According to the statistical time periods of 2000-2020, WSVR, AIG-SVR, and BWO-SVR hybrid models in the combined structure including all input parameters had better performance due to higher memory, and WSVR model was more accurate with less error due to

Therefore, due to the decline in groundwater levels over the next 20 years, it is recommended that smart meters be installed on all agricultural and industrial wells and the drilling of illegal wells be prevented. In addition, the owners of agricultural wells need to be informed and notified of the dangers associated with the cultivation of hydrophilic crops. This strategy is useful and facilitates the development and implementation of groundwater management strategies and it is a step forward towards management decisions to improve the quantity of groundwater resources.