Groundwater Level Prediction Using Modified Linear Regression

Kommineni, Madhuri; Reddy, K Veniha; Jagathi, K; Reddy, B. Dushyanth; Roshini, A; Bhavani, Vasantha

doi:10.1109/icaccs48705.2020.9074313

Cited by 8 publications

(3 citation statements)

References 1 publication

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“…Regression analysis was used to predict groundwater levels (Sahoo & Jha 2013;Yan et al 2018;Kommineni et al 2020), recharge (Lorenz & Delin 2007), and groundwater quality (Chenini & Khemiri 2009). In this work, a multi-regression analysis was used to estimate both river filtrate portion (Equation (4)) and minimum travel time (Equation ( 5)) as functions of pumping rate and distance between the pumping well and the river (Figure 9).…”

Section: Multi-regression Analysismentioning

confidence: 99%

Studying the effect of design parameters on riverbank filtration performance for drinking water supply in Egypt: a case study

Mamdouh

Wahaab²,

Khalifa

et al. 2021

Water Supply

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Riverbank filtration (RBF) is an affordable technique to provide drinking water with adequate quality. The ultimate objective of this study is to facilitate the transferability and application of this sustainable technique in Egypt. In this work, a numerical model was constructed using Groundwater Modeling System (GMS) to study the effect of four design parameters on the RBF performance parameters (i.e., river filtrate portion and travel time) with the aid of MODPATH and ZONEBUDGET. The design parameters were; (1) the pumping rates of the RBF wells, (2) number of operating wells, (3) distance between wells and the river, and (4) the spacing between wells. This study was focused on the hydraulic aspects of the technique. The results demonstrated that; (1) the river filtrate portion exceeds 75% regardless the design conditions. (2) The hydraulic performance of RBF technique is highly controlled by the production capacity of the wells and their positions relative to the surface water systems; the spacing between wells has a minimum effect. Two equations were developed to estimate the river filtrate portion and minimum travel time as functions of pumping rate and distance between the pumping well and the river.

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Section: Multi-regression Analysismentioning

confidence: 99%

Studying the effect of design parameters on riverbank filtration performance for drinking water supply in Egypt: a case study

Mamdouh

Wahaab²,

Khalifa

et al. 2021

Water Supply

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show abstract

“…Traditional methods for predicting water level can be categorized into six types: (1) modified linear regression [2]; (2) the wavelet-ANFIS model [3]; (3) the hydrodynamic model [4]; (4) the particle filtering algorithm [5]; (5) the Bayesian vine copula (BVC) model [6]; and (6) the autoregressive integrated moving average (ARIMA) model [7]. Modified linear regression employs the partial least squares (PLS) regression technique, which involves assessing the interconnections among influential factors.…”

Section: Introductionmentioning

confidence: 99%

WLP-VBL: A Robust Lightweight Model for Water Level Prediction

Yi,

Huang,

Pan

et al. 2023

Electronics

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Accurate and reliable water level prediction plays a crucial role in the optimal management of water resources and reservoir scheduling. Water level data have the characteristics of volatility and temporality; a single water level prediction model can only be applied to specific hydrological conditions and reservoirs. Therefore, in this paper, we present a robust lightweight model for water level prediction, namely WLP-VBL, by using a combination of VMD, BA, and LSTM. The proposed WLP-VBL model consists of three steps: first, the water level dataset is decomposed by EMD to obtain a number of decomposition layers K, and then VMD is used to decompose the original water level dataset into K intrinsic modal functions (IMFs) to produce a clearer signal. Next, the IMF data are sent to an LSTM neural network optimized by BA for prediction, and finally each component is superimposed to obtain the predicted value. In order to evaluate the effectiveness of the model, experiments were carried out on water level data for the Gan River. The results indicate that: (1) Compared with state-of-the art methods, e.g., LSTM, VMD-LSTM, and EMD-LSTM, WLP-VBL exhibited the best performance. The MSE and MAE of WLP-VBL decreased by 69.6~74.7% and 45~98.5%, respectively. (2) The proposed model showed stronger robustness for water level prediction, and was able to handle highly volatile and noisy data.

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“…Traditional data gathering and analysis methods have been challenged by creative innovations such as Internet of Things (IoT) sensors, cloud computing, and advanced data processing tools [9]. This study pioneers a novel method of groundwater management by seamlessly combining IoT sensors, cloud-based computing, and sophisticated algorithms [10].…”

Section: Introductionmentioning

confidence: 99%

Predicting Groundwater Level using Temporal Attention Enhanced Graph Neural Network

Sowndharya M, Duraisamy S, Pavithra M

2023

tjjpt

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A revolutionary approach for groundwater management is essential. To predicting the water levels using for integrating IoT sensors, cloud computing, and advanced data analysis methods. IoT sensors are employed for real-time measurement of groundwater levels, creating a robust dataset. The paper focuses on predicting future groundwater levels, a crucial aspect of sustainable resource management. To enhance predictive accuracy, a preprocessing algorithm, such as Min-Max normalization, is introduced to clean and normalize the collected data, ensuring its reliability. Additionally, a feature extraction algorithm, such as Principal Component Analysis (PCA), is implemented to identify relevant patterns and trends within the dataset, enhancing the efficiency of subsequent analysis. A novel classification algorithm, Spatial-Temporal Graph Convolutional Network, is introduced, enabling the identification of potential groundwater recharge areas. This classification algorithm leverages historical data and extracted features to categorize regions based on their suitability for groundwater renewal. Finally, this research uses a Temporal attention-enhanced graph Neural Network machine learning algorithm to predict groundwater levels in the next few years. This algorithm utilizes the preprocessed data and extracted features, identifying intricate patterns and trends in historical data to generate precise predictions for groundwater levels in the upcoming years.

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Groundwater Level Prediction Using Modified Linear Regression

Cited by 8 publications

References 1 publication

Studying the effect of design parameters on riverbank filtration performance for drinking water supply in Egypt: a case study

Studying the effect of design parameters on riverbank filtration performance for drinking water supply in Egypt: a case study

WLP-VBL: A Robust Lightweight Model for Water Level Prediction

Predicting Groundwater Level using Temporal Attention Enhanced Graph Neural Network

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