Modeling of multiple regression and multiple linear regressions for prediction of groundwater quality (case study: north of Shiraz)

Aghaei

IET Renewable Power Gen

et al. 2023

The study aims to predict solar energy generation to ensure the successful operation of solar power plants. This objective is crucial in light of the increasing energy demand, global warming concerns, and greenhouse gas emissions. To achieve this, the study employs multiple linear regression and feature selection techniques to calculate energy generation. Additionally, long short‐term memory (LSTM) is used to predict energy generation levels based on climate conditions. Furthermore, the spatial distribution of energy generation is analyzed using inverse distance weighting. The results of the study reveal that temperature, solar radiation, relative humidity, wind speed, wind direction, and vapor pressure deficit are the most significant parameters for predicting energy generation. The LSTM method proves to be highly accurate in predicting fluctuating energy generation patterns. Notably, the southern regions of the study area exhibit a greater potential for energy generation compared to the northern regions. Approximately 30% of the region generates over 1400 kWh, with the southern areas, characterized by hot and dry climates, producing around 1500 kWh, while the northern regions, with cold and humid climates, generate approximately 1100 kWh.

Section: Discussionsupporting

confidence: 92%

Geographic information system‐based prediction of solar power plant production using deep neural networks

Aghaei

IET Renewable Power Gen

et al. 2023

“…Physicochemical parameters of groundwater Table 2 shows the hydrochemical properties of groundwater quality. Recognizing the groundwater quality is significant as it is the key feature defining its appropriateness for drinking, agricultural and industrial purposes (Mokarram 2016). Table 2 abridges the outcomes of several parameters comprising statistical measures such as average, standard deviation, kurtosis and skewness analyzed of groundwater samples from the study area.…”

Section: Resultsmentioning

confidence: 99%

Evaluation of groundwater quality and its suitability for drinking and irrigation using GIS and geostatistics techniques in semiarid region of Neyshabur, Iran

Bhunia¹,

Keshavarzi

Shit³

et al. 2018

Appl Water Sci

Groundwater is a vital source for drinking and agricultural purposes in semiarid region of Neyshabur area (Iran). The present study assessed the groundwater quality and mapped the spatial variation of water samples in terms of suitability for drinking and irrigation purposes. A total 402 groundwater samples were collected from the field with global positioning system (GPS) from 2010 to 2013 and analyzed for pH, calcium (Ca 2+ ), magnesium (Mg 2+ ), sodium (Na + ), potassium, bicarbonate, sulfate, chloride, sodium adsorption ratio (SAR), electrical conductivity (EC), total dissolved solids, and total hardness (TH). A GIS-based ordinary kriging method with best fit semivariogram models was used for preparation of thematic maps of groundwater quality parameters. The results were evaluated and compared with WHO (2011) recommended water quality standard. Results showed that 68.40% of SAR, 25% of Mg 2+ , 32.62% of Na + , and 1.74% of TH of the total groundwater samples are suitable for the irrigation purpose. Consequently, 55.57% of EC, 89.19% of TDS, 0.75% of pH, and 6.25% of K + of the total groundwater samples are suitable for the drinking purpose as per the WHO standard. The groundwater quality in the study area is very hard and slightly alkaline in nature. The spatial distribution map of groundwater quality showed 80% of the area suitable for drinking purpose; whereas, 90% of the area demarcated for irrigation purpose.

“…The Multiple Regression Analysis (MRA) is established based on the correlation among hydrochemical variables, and it is widely applied in groundwater studies [41][42][43][44][45].…”

Section: 3multiple Regression Analysismentioning

confidence: 99%

Application of Geochemical Modelling and Multiple Regression Analysis to Reassess Groundwater Evolution in Kaduna Basin, NW Nigeria

Wali,

Alias,

Harun

et al. 2024

Preprint

This review reassesses groundwater's hydrochemical evolution in the Kaduna Basin using geochemical modelling (GM) and multiple regression analysis (MRA). Hydrochemical data were obtained from the literature using a systematic approach and were subjected to GM and MRA. The geochemical modelling results showed that aquifers are saturated with Anhydrite, Chlorite, Chrysotile, Dolomite, Gypsum, Halite, K-Mica, Pyrite, Quartz, Dolomite, and Goethite. The Na2O-Al2O3-SiO2-H2O system indicated that the studied locations fell in Na-montmorillonite and Kalinite fields. However, the K2OAl2O3-SiO2-H2O system showed that groundwater is stable with Montmorillonite, Kaolinite, and K-Feldspar. MRA and Na+/Cl- molar ratio revealed that rock weathering significantly controls groundwater. The Na+/Cl- values are above 1 in 83.33%, 54.84%, and 26.27% sampling locations in the Kudenda-Nassarawa area, Kaduna South, and Kakuri and its Environs, respectively. The overall p-value was less than 0.01 and R-Sq = 63%, suggesting that all elements significantly influence aquifers' hydrochemistry. Results of geochemical modelling, Na+/Cl- molar ratio, and MRA are concurrent as they both show silicate weathering as the factor controlling groundwater hydrochemistry. Thus, geochemical and multiple regression analysis offers a reliable and user-friendly tool for assessing aquifers' hydrochemistry. We hope this review's findings will stimulate other researchers to an analogous procedure in a forthcoming study on hydrochemical analysis, especially those from semi-arid environments.