Modeling the fluctuations of groundwater level by employing ensemble deep learning techniques

Abstract

This study proposes two techniques: Deep Learning (DL) and Ensemble Deep Learning (EDL) to predict groundwater level (GWL) for five wells in Malaysia. Two scenarios were proposed, scenario-1 (S1): GWL from 4 wells was used as inputs to predict the GWL in the fifth well and scenario-2 (S2): time series with lag time up to 20 days for all five wells. The results from S1 prove that the ensemble EDL generally performs superior to the DL in the estimation of GWL of each station using data of remaining four wells except the Paya Indah Wetland in which the DL method provide better estimates compared to EDL. Regarding S2, the EDL also exhibits superior performance in predicting daily GWL in all five stations compared to the DL model. Implementing EDL decreased the RMSE, NAE and RRMSE by 11.6%, 27.3% and 22.3% and increased the R, Spearman rho and Kendall tau by 0.4%, 1.1% and 3.5%, respectively. Moreover, EDL for S2 shows a high level of precision within less time lag, ranging between 2 and 4 compared to DL. Therefore, the EDL model has the potential in managing the sustainability of groundwater in Malaysia.

Using Remote Sensing and Machine Learning to Locate Groundwater Discharge to Salmon-Bearing Streams

“…The ways remote sensing, geospatial modeling, and/or machine learning are used in hydrologic studies depends on the question being addressed; the spatial and temporal scale of the question; and the type, amount, and quality of the available data [24][25][26]. Nevertheless, these tools have been incorporated into strategies to forecast groundwater levels [27][28][29][30], groundwater quality [31][32][33], saltwater intrusion and groundwater salinity [34], and groundwater resource availability [35,36]. Using these approaches to better understand and predict groundwater discharge is particularly challenging (e.g., [22,23]).…”

Cooperative Multi-Robot Task Allocation with Reinforcement Learning

“…We empirically found that population size N generated children of size ≈ N 2 and that evaluating all of these solutions was computationally expensive. Therefore we set the population size 10 which shows the most increasing performance among five options [5,10,20,30,50]. All these parameters were tuned with problem instances with five robots and 50 tasks.…”

“…We set the number of robots, the map size, and the workload to 5, 100, and 20, respectively. The tasks are randomly distributed in 100 × 100 continuous 2D space and the initial workload for each robot is sampled uniformly in a set range [1,20]. We tested 10, 20, 30, 40, and 50 tasks to compare the performances of thg algorithms on various difficulty levels.…”

“…Therefore we compare the solution of branch and bound method of the mixed integer programming and meta-heuristic in the RL environment. We test on 100 random problem instances with 3 robots and 3 tasks in map size 100 and uniform workload in [1,20]. There are (3!)…”

“…Recent advances in deep learning have led to a breakthrough in various fields of reallife applications including MRTA, aquaculture, thermal processes, energy system, mobile network, and vehicles [16][17][18][19][20][21][22]. The deep learning is applicable to various fields because of the high-dimensional representation that can encode complex knowledge in the real world.…”