Groundwater Level Prediction for the Arid Oasis of Northwest China Based on the Artificial Bee Colony Algorithm and a Back-propagation Neural Network with Double Hidden Layers

Abstract

Groundwater is crucial for economic and agricultural development, particularly in arid areas where surface water resources are extremely scarce. The prediction of groundwater levels is essential for understanding groundwater dynamics and providing scientific guidance for the rational utilization of groundwater resources. A back propagation (BP) neural network based on the artificial bee colony (ABC) optimization algorithm was established in this study to accurately predict groundwater levels in the overexploited arid areas of Northwest China. Recharge, exploitation, rainfall, and evaporation were used as input factors, whereas groundwater level was used as the output factor. Results showed that the fitting accuracy, convergence rate, and stabilization of the ABC-BP model are better than those of the particle swarm optimization (PSO-BP), genetic algorithm (GA-BP), and BP models, thereby proving that the ABC-BP model can be a new method for predicting groundwater levels. The ABC-BP model with double hidden layers and a topology structure of 4-7-3-1, which overcame the overfitting problem, was developed to predict groundwater levels in Yaoba Oasis from 2019 to 2030. The prediction results of different mining regimes showed that the groundwater level in the study area will gradually decrease as exploitation quantity increases and then undergo a decline stage given the existing mining condition of 40 million m3/year. According to the simulation results under different scenarios, the most appropriate amount of groundwater exploitation should be maintained at 31 million m3/year to promote the sustainable development of groundwater resources in Yaoba Oasis.

Seasonal and Inter-Annual Variability of Groundwater and Their Responses to Climate Change and Human Activities in Arid and Desert Areas: A Case Study in Yaoba Oasis, Northwest China

“…The previous studies have indicated that the amount of groundwater recharge is maintained at approximately 31 million m 3 /year, while the total amount of exploitation is maintained at 40 million m 3 /year in Yaoba Oasis [46]. Although the groundwater level slowly recovers during the year, the recharge cannot still meet the exploitation, resulting in the gradual decline in groundwater level year after year.…”

Dimension Reduction of Machine Learning-Based Forecasting Models Employing Principal Component Analysis

“…On the other hand, machine learning-based models as statistical approaches have shown the great capability for simulating and forecasting of complicated phenomena. These techniques have been successfully applied to forecasting purposes for a large number of real-world applications such as river flow forecasting and hydrological modeling [4][5][6][7], water quality predictions [8][9][10], and groundwater level estimations [11][12][13], etc. Detailed descriptions of big data in complex and social networks are presented in [14].…”

Prediction of groundwater level in basement complex terrain using artificial neural network: a case of Ijebu-Jesa, southwestern Nigeria

“…Groundwater level is an indicator of groundwater availability, groundwater flow and the physical characteristics of an aquifer or groundwater system (Nair and Sindhu 2016). A decrease in groundwater levels can trigger a number of eco-environmental problems capable of seriously affecting both local agricultural production and economic development (Li et al 2019). Groundwater level is an important indicator of groundwater balance.…”

“…Influence of climatic factors and human activities can make groundwater level exhibits cyclical and random characteristics. Therefore, the accurate prediction of groundwater level is of great significance for the rational utilization of groundwater resources and the sustainable development of the social economy (Li et al 2019).…”