## 4 Bigram and Traditional Results
### Forecasting Water Levels
#### 4.3.2 Traditional Analysis
\begin{table}
\begin{tabular}{c c c c} \hline
**Techniques** & **Metrics** & **Data** & **Application** \\ \hline  & & The study used datasets from various sources, including precipitation from China & \\  & The results show that the XGB algorithm with the Twoedies has function achieved & Meteorological Data Service Centre, & \\ XGB, M.R.E, GWR, SVR, the best performance with an X-value & from C.N.U. prediction index from NASA & To present a framework using the XGB machine learning method for learning groundwater \\ RF, DW, OK, COK & compared to other machine training and & bank geographic database, find cover data & \\  & interpolation methods like M.R. GWR, & from ESA, histology data from the Spatial & \\  & SVR, RF, IDW, OK, and OK, etc. & Unique of Digital Ecology Map & \\ \hline  & & The most significant variations in predicting & \\  & & integration water demand were found to be & \\  & integrated compared to aft temperature, and & Resources Agency and gridMT to predict & \\ Gaussian process regression & & normal irrigation water demand. They simplified & \\  & process regression model showed high & 400- comments into 20 crop categories and & \\  & accuracy, with an R2 higher than 0/50 & multi-crop areas for modeling. & \\  & & different input variable combinations. & \\ \hline  & & The study discovered 30 classes through & \\  & H\(\alpha\), with high regulatory network levels in & \\  & the western part of the Ogilla Aquirir & & \\  & that decreased towards the east. The ANN & The study is based on the time series of & To present a study that employs HCA and ANNs to predict annual groundwater levels in & \\  & artificial neural networks (ANNs) & non-existent walls, and integrating HCA & 403 wells of the Ogilla Aquir, & \\  & and ANN allowed for deriving annual & & \\  & groundwater level forecasting for well & \\  & sets. & & \\ \hline  & & Two datasets were used in this research. & \\  & The XGBoost model, with R2 = 0.86 and & Landsat 801 OL project, which collected multispectral imagery of the Earth’s & \\  & RMSE = 0.29, outperformed other models. & surface to derive H\(\alpha\) links across & \\  & The entire dataset of H\(\alpha\) held a strong & & \\  & association with Landsat reflectance, & & \\  & The H\(\alpha\) decreased from 2015 to 2020. & & \\ \hline \end{tabular}
\end{table} 
Table 3: _Cont._The study comparing the performance of support vector regression (SVR) and convolutional neural networks (CNN) in groundwater potential mapping in Damyang, South Korea, showed that both models effectively predicted groundwater potential, with CNN exhibiting slightly higher accuracy [69]. This suggests that deep learning algorithms, such as CNN, can enhance spatial predictions for sustainable groundwater management and aid in developing effective groundwater exploitation strategies. In a separate study, a Gradient Tree Boosting (GTB)-based method for predicting water levels in irrigation and drainage systems was developed, addressing the limitations of traditional hydrological models that demand extensive data and computing time [72]. The GTB model, developed using data from Vietnam's Bac-Hung-Hai system with 3348 samples collected between 2000 and 2020, was compared to eight common machine learning algorithms for performance evaluation. The GTB-based method provides an efficient solution for sustainable water management and regulation in the face of accelerated urbanization and climate change. In [73], the authors aim to evaluate and contrast the efficacy of three statistical algorithms (Random Forest, XGBoost, and Multilinear Regression) for spatiotemporal modeling of groundwater level dynamics in a vast tropical peatland located in Central Kalimantan, Indonesia. The employed dataset consists of comprehensive field data collected between 2010 and 2012, incorporating variables such as distance from canal, elevation, precipitation, and evapotranspiration. The study's goal is to shed light on the importance of crucial physical system parameters and climatic influences in predicting GWL.