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  1. Book ; Online ; E-Book: Groundwater resources development and planning in the semi-arid region

    Pande, Chaitanya B. / Moharir, Kanak N.

    2021  

    Author's details Chaitanya B. Pande, Kanak N. Moharir, editors
    Keywords Groundwater/Management
    Subject code 553.79
    Language English
    Size 1 online resource (572 pages)
    Publisher Springer
    Publishing place Cham, Switzerland
    Document type Book ; Online ; E-Book
    Remark Zugriff für angemeldete ZB MED-Nutzerinnen und -Nutzer
    ISBN 3-030-68124-6 ; 3-030-68123-8 ; 978-3-030-68124-1 ; 978-3-030-68123-4
    Database ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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  2. Article ; Online: Land use/land cover and change detection mapping in Rahuri watershed area (MS), India using the google earth engine and machine learning approach

    Pande, Chaitanya B.

    Geocarto International. 2022 Dec. 13, v. 37, no. 26 p.13860-13880

    2022  

    Abstract: The change detection and land use and land cover (LULC) maps are more important powerful forces behind numerous ecological systems and fallow land. The current research focuses on demarcating the spatiotemporal LULC changes, NDVI and change detections ... ...

    Abstract The change detection and land use and land cover (LULC) maps are more important powerful forces behind numerous ecological systems and fallow land. The current research focuses on demarcating the spatiotemporal LULC changes, NDVI and change detections maps. These effects directly affect the ecosystem, land resources, cropping pattern and agriculture. LULC assessment and surveillance are essential for long-term planning and sustainable use of natural resources. However, we have developed the soft computing machine learning algorithm for mapping land use and land cover based on the Google earth engine (GEE) platform and change detection mapping done by SAGA GIS software. It is significantly used for ecological safety and planning under various climate variations. To accurately describe the land use and land cover classes with changes are identified in the area. This area exclusively uses the multitemporal Landsat-5 (30 m) and Sentinel-2 (10 m) imageries in LULC mapping. The GEE is a cloud-computing platform with the prevailing classification ability of random forest (RF) models to make five-year interval LULC maps for 2010, 2015 and 2020. To unique multiple RF models established as a classifier in the algorithm created by JavaScript and GEE. SAGA GIS has provided the best platform for detecting changes in land use and land cover classes. NDVI maps are created based on the cloud-based platform. These maps value ranges between −0.68 to −0.15, 0.76 to −0.29 and 0.66 to −0.11 in 2010, 2015 and 2020. Experimental outcomes indicate five classes such as water bodies, built up, barren, cropland and fallow land during 2010, 2015 and 2020. The overall accuracy of User and Producer for 2010, 2015 and 2019 years in between 86.23%, 88.34%, 85.53% and 92.51%, 94.34% and 91.54%, respectively. We have observed that (2010, 2015 − 2020) agriculture and built-up land increased by 1040.76 ha, 1246.32 ha, 1500.93 ha and 34.96 ha, 37.08 ha, 42.58 ha, respectively. Other side degraded land, fallow land, waterbodies areas (953.19 ha, 679.23 ha, 937.24 ha and 1385.73 ha, 1513.53 ha, 991.08 ha and 32.85 ha, 21.33 ha, 25.66 ha) are increased during the year of 2010, 2015 and 2020, respectively. While results have been done by GEE cloud platform and remote sensing data, this developed algorithm easily classified the land use maps from Landsat-5 and Sentinel-2 TM imagery in the machine learning approach. The determined 30-m and 10-m three-year LULC maps are made-up to deliver vital data on the changes, monitoring and understanding of which types of LULC classes and changes have occupied a place in the Rahuri area.
    Keywords Internet ; algorithms ; climate ; computer software ; cropland ; ecosystems ; land cover ; land use ; land use and land cover maps ; monitoring ; surface water ; watersheds ; India ; Google earth engine ; random forest ; classification ; LULC ; remote sensing
    Language English
    Dates of publication 2022-1213
    Size p. 13860-13880.
    Publishing place Taylor & Francis
    Document type Article ; Online
    ISSN 1752-0762
    DOI 10.1080/10106049.2022.2086622
    Database NAL-Catalogue (AGRICOLA)

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  3. Article ; Online: Prediction of weighted arithmetic water quality index for urban water quality using ensemble machine learning model.

    Mohseni, Usman / Pande, Chaitanya B / Chandra Pal, Subodh / Alshehri, Fahad

    Chemosphere

    2024  Volume 352, Page(s) 141393

    Abstract: Urban water quality index (WQI) is an important factor for assessment quality of groundwater in the urban and rural area. In this research, the Weighted Arithmetic Water Quality Index (WA-WQI) was estimated for understanding the groundwater quality. Four ...

    Abstract Urban water quality index (WQI) is an important factor for assessment quality of groundwater in the urban and rural area. In this research, the Weighted Arithmetic Water Quality Index (WA-WQI) was estimated for understanding the groundwater quality. Four machine learning (ML) models were developed including artificial neural network (ANN), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XG-Boost) in addition to multiple linear regression (MLR) for WA-WQI prediction at the Ujjain city of Madhya Pradesh in India. Groundwater quality samples were collected from 54 wards under the urban area, the main eight different physiochemical parameters were selected for WA-WQI prediction. The different input parameters data were analysed and calculated for the relationships of their ability to predict the results of WA-WQI. The ML models performance were calculated using three statistical metrics such as determination coefficient (R
    MeSH term(s) Water Quality ; Machine Learning ; Neural Networks, Computer ; Groundwater ; Linear Models
    Language English
    Publishing date 2024-02-05
    Publishing country England
    Document type Journal Article
    ZDB-ID 120089-6
    ISSN 1879-1298 ; 0045-6535 ; 0366-7111
    ISSN (online) 1879-1298
    ISSN 0045-6535 ; 0366-7111
    DOI 10.1016/j.chemosphere.2024.141393
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Monitoring of wetland turbidity using multi-temporal Landsat-8 and Landsat-9 satellite imagery in the Bisalpur wetland, Rajasthan, India.

    Singh, Raj / Saritha, Vara / Pande, Chaitanya B

    Environmental research

    2023  Volume 241, Page(s) 117638

    Abstract: Satellite imagery has emerged as the predominant method for performing spatial and temporal water quality analyses on a global scale. This study employs remote sensing techniques to monitor the water quality of the Bisalpur wetland during both the pre ... ...

    Abstract Satellite imagery has emerged as the predominant method for performing spatial and temporal water quality analyses on a global scale. This study employs remote sensing techniques to monitor the water quality of the Bisalpur wetland during both the pre and post-monsoon seasons in 2013 and 2022. The study aims to investigate the prospective use of Landsat-8 (L8) and Landsat-9 (L9) data acquired from the Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) for the temporal monitoring of turbidity. Concurrently, the study examines the relationship of turbidity with water surface temperature (WST) and chlorophyll-a (Chl-a) concentrations. We utilized visible and near-infrared (NIR) bands to conduct a single-band spectral response analysis of wetland turbidity. The results reveal a notable increase in turbidity concentration in May 2022, as this timeframe recorded the highest reflectance (0.28) in the NIR band. Additionally, the normalized difference turbidity index (NDTI) formula was used to assess the overall turbidity levels in the wetland. The results indicated that the highest concentration was observed in May 2013, with a value of 0.37, while the second-highest concentration was recorded in May 2022, with a value of 0.25. The WST was calculated using thermal band-10 in conjunction with Chlorophyll-a, utilizing the normalized difference chlorophyll index (NDCI). The regression analysis shows a positive correlation between turbidity and WST, as indicated by R2 values of 0.41 in May 2013 and 0.40 in May 2022. Furthermore, a robust positive relationship exists between turbidity and Chl-a, with a high R2 value of 0.71 in May 2022. These findings emphasize the efficacy of the L8 and L9 datasets for conducting temporal analyses of wetland turbidity, WST, and Chl-a. Additionally, this research underscores the critical role of satellite imagery in assessing and managing water quality, particularly in situations where in-situ data is lacking.
    MeSH term(s) Satellite Imagery ; Wetlands ; Environmental Monitoring/methods ; India ; Chlorophyll A/analysis ; Chlorophyll/analysis
    Chemical Substances Chlorophyll A (YF5Q9EJC8Y) ; Chlorophyll (1406-65-1)
    Language English
    Publishing date 2023-11-14
    Publishing country Netherlands
    Document type Journal Article
    ZDB-ID 205699-9
    ISSN 1096-0953 ; 0013-9351
    ISSN (online) 1096-0953
    ISSN 0013-9351
    DOI 10.1016/j.envres.2023.117638
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: Integration of hydrogeological data, GIS and AHP techniques applied to delineate groundwater potential zones in sandstone, limestone and shales rocks of the Damoh district, (MP) central India.

    Moharir, Kanak N / Pande, Chaitanya B / Gautam, Vinay Kumar / Singh, Sudhir Kumar / Rane, Nitin Liladhar

    Environmental research

    2023  Volume 228, Page(s) 115832

    Abstract: The Damoh district, which is located in the central India and characterized by limestone, shales, and sandstone compact rock. The district has been facing groundwater development challenges and problems for several decades. To facilitate groundwater ... ...

    Abstract The Damoh district, which is located in the central India and characterized by limestone, shales, and sandstone compact rock. The district has been facing groundwater development challenges and problems for several decades. To facilitate groundwater management, it is crucial to monitoring and planning based on geology, slope, relief, land use, geomorphology, and the types of the basaltic aquifer in the drought-groundwater deficit area. Moreover, the majority of farmers in the area are heavily dependent on groundwater for their crops. Therefore, delineation of groundwater potential zones (GPZ) is essential, which is defined based on various thematic layers, including geology, geomorphology, slope, aspect, drainage density, lineament density, topographic wetness index (TWI), topographic ruggedness index (TRI), and land use/land cover (LULC). The processing and analysis of this information were carried out using Geographic Information System (GIS) and Analytic Hierarchy Process (AHP) methods. The validity of the results was trained and tested using Receiver Operating Characteristic (ROC) curves, which showed training and testing accuracies of 0.713 and 0.701, respectively. The GPZ map was classified into five classes such as very high, high, moderate, low, and very low. The study revealed that approximately 45% of the area falls under the moderate GPZ, while only 30% of the region is classified as having a high GPZ. The area receives high rainfall but has very high surface runoff due to no proper developed soil and lack of water conservation structures. Every summer season show a declined groundwater level. In this context, results of study area are useful to maintain the groundwater under climate change and summer season. The GPZ map plays an important role in implementing artificial recharge structures (ARS), such as percolation ponds, tube wells, bore wells, cement nala bunds (CNBs), continuous contour trenching (CCTs), and others for development of ground level. This study is significant for developing sustainable groundwater management policies in semi-arid regions, that are experiencing climate change. Proper groundwater potential mapping and watershed development policies can help mitigate the effects of drought, climate change, and water scarcity, while preserving the ecosystem in the Limestone, Shales, and Sandstone compact rock region. The results of this study are essential for farmers, regional planners, policy-makers, climate change experts, and local governments, enabling them to understand the groundwater development possibilities in the study area.
    MeSH term(s) Geographic Information Systems ; Calcium Carbonate/analysis ; Analytic Hierarchy Process ; Ecosystem ; Environmental Monitoring/methods ; Groundwater/analysis ; India
    Chemical Substances Calcium Carbonate (H0G9379FGK)
    Language English
    Publishing date 2023-04-11
    Publishing country Netherlands
    Document type Journal Article
    ZDB-ID 205699-9
    ISSN 1096-0953 ; 0013-9351
    ISSN (online) 1096-0953
    ISSN 0013-9351
    DOI 10.1016/j.envres.2023.115832
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article ; Online: Groundwater quality characterization using an integrated water quality index and multivariate statistical techniques.

    Gautam, Vinay Kumar / Kothari, Mahesh / Al-Ramadan, Baqer / Singh, Pradeep Kumar / Upadhyay, Harsh / Pande, Chaitanya B / Alshehri, Fahad / Yaseen, Zaher Mundher

    PloS one

    2024  Volume 19, Issue 2, Page(s) e0294533

    Abstract: This study attempts to characterize and interpret the groundwater quality (GWQ) using a GIS environment and multivariate statistical approach (MSA) for the Jakham River Basin (JRB) in Southern Rajasthan. In this paper, analysis of various statistical ... ...

    Abstract This study attempts to characterize and interpret the groundwater quality (GWQ) using a GIS environment and multivariate statistical approach (MSA) for the Jakham River Basin (JRB) in Southern Rajasthan. In this paper, analysis of various statistical indicators such as the Water Quality Index (WQI) and multivariate statistical methods, i.e., principal component analysis and correspondence analysis (PCA and CA), were implemented on the pre and post-monsoon water quality datasets. All these methods help identify the most critical factor in controlling GWQ for potable water. In pre-monsoon (PRM) and post-monsoon (POM) seasons, the computed value of WQI has ranged between 28.28 to 116.74 and from 29.49 to 111.98, respectively. As per the GIS-based WQI findings, 63.42 percent of the groundwater samples during the PRM season and 42.02 percent during the POM were classed as 'good' and could be consumed for drinking. The Principal component analysis (PCA) is a suitable tool for simplification of the evaluation process in water quality analysis. The PCA correlation matrix defines the relation among the water quality parameters, which helps to detect the natural or anthropogenic influence on sub-surface water. The finding of PCA's factor analysis shows the impact of geological and human intervention, as increased levels of EC, TDS, Na+, Cl-, HCO3-, F-, and SO42- on potable water. In this study, hierarchical cluster analysis (HCA) was used to categories the WQ parameters for PRM and POR seasons using the Ward technique. The research outcomes of this study can be used as baseline data for GWQ development activities and protect human health from water-borne diseases in the southern region of Rajasthan.
    MeSH term(s) Humans ; Water Quality ; Environmental Monitoring/methods ; Drinking Water/analysis ; Water Pollutants, Chemical/analysis ; India ; Groundwater/analysis
    Chemical Substances Drinking Water ; Water Pollutants, Chemical
    Language English
    Publishing date 2024-02-23
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2267670-3
    ISSN 1932-6203 ; 1932-6203
    ISSN (online) 1932-6203
    ISSN 1932-6203
    DOI 10.1371/journal.pone.0294533
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article ; Online: Drought indicator analysis and forecasting using data driven models: case study in Jaisalmer, India

    Elbeltagi, Ahmed / Manish Kumar / Kushwaha, N. L. / Pande, Chaitanya B. / Ditthakit, Pakorn / Vishwakarma, Dinesh Kumar / Subeesh, A.

    Stoch Environ Res Risk Assess. 2023 Jan., v. 37, no. 1 p.113-131

    2023  

    Abstract: Agricultural droughts are a prime concern for economies worldwide as they negatively impact the productivity of rain-fed crops, employment, and income per capita. In this study, Standard Precipitation Index (SPI) has been used to evaluate different ... ...

    Abstract Agricultural droughts are a prime concern for economies worldwide as they negatively impact the productivity of rain-fed crops, employment, and income per capita. In this study, Standard Precipitation Index (SPI) has been used to evaluate different drought indices for Rajasthan of India. In agricultural, hydrological, and meteorological applications such as irrigation scheduling, crop simulation, water budgeting, reservoir operations, and weather forecasting, the accurate estimation of the drought indices such as the Standardized Precipitation Index (SPI) plays an important role. Thus, the present study was conducted to examine the feasibility and effectiveness of the Random Subspace (RSS) model and its hybridization with the M5 Pruning tree (M5P), Random Forest (RF), and Random Tree (RT) to estimate the SPI at 3, 6, and 12 droughts during 2000–2019. Performances of RSS and hybridized algorithms were assessed and compared using performance indicators (i.e., MAE, RMSE, RAE, RRSE, and R²) and various graphical interpretations. Results indicated that the RSS-M5P provided the most accurate SPI prediction (MAE = 0.497, RMSE = 0.682, RAE = 81.88, RRSE = 87.22, and R² = 0.507 for SPI-3; MAE = 0.452, RMSE = 0.717, RAE = 69.76, RRSE = 85.24, and R² = 0.402 for SPI-6. And MAE = 0.294, RMSE = 0.377, RAE = 55.79, RRSE = 59.57, and R² = 0.783 for SPI-12) compare to RSS alone, RSS-RF, and RSS-RT models for study the drought situation in Jaisalmer Rajasthan. The M5P algorithms have improved the performance of the RSS structure.
    Keywords atmospheric precipitation ; case studies ; drought ; employment ; hybridization ; hydrology ; income ; irrigation ; models ; prediction ; risk ; trees ; India
    Language English
    Dates of publication 2023-01
    Size p. 113-131.
    Publishing place Springer Berlin Heidelberg
    Document type Article ; Online
    ZDB-ID 1481263-0
    ISSN 1436-3259 ; 1435-151X ; 1436-3240 ; 0931-1955
    ISSN (online) 1436-3259 ; 1435-151X
    ISSN 1436-3240 ; 0931-1955
    DOI 10.1007/s00477-022-02277-0
    Database NAL-Catalogue (AGRICOLA)

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  8. Article ; Online: Integration of hydrogeological data, GIS and AHP techniques applied to delineate groundwater potential zones in sandstone, limestone and shales rocks of the Damoh district, (MP) central India

    Moharir, Kanak N. / Pande, Chaitanya B. / Gautam, Vinay Kumar / Singh, Sudhir Kumar / Rane, Nitin Liladhar

    Environmental Research. 2023 July, v. 228 p.115832-

    2023  

    Abstract: The Damoh district, which is located in the central India and characterized by limestone, shales, and sandstone compact rock. The district has been facing groundwater development challenges and problems for several decades. To facilitate groundwater ... ...

    Abstract The Damoh district, which is located in the central India and characterized by limestone, shales, and sandstone compact rock. The district has been facing groundwater development challenges and problems for several decades. To facilitate groundwater management, it is crucial to monitoring and planning based on geology, slope, relief, land use, geomorphology, and the types of the basaltic aquifer in the drought-groundwater deficit area. Moreover, the majority of farmers in the area are heavily dependent on groundwater for their crops. Therefore, delineation of groundwater potential zones (GPZ) is essential, which is defined based on various thematic layers, including geology, geomorphology, slope, aspect, drainage density, lineament density, topographic wetness index (TWI), topographic ruggedness index (TRI), and land use/land cover (LULC). The processing and analysis of this information were carried out using Geographic Information System (GIS) and Analytic Hierarchy Process (AHP) methods. The validity of the results was trained and tested using Receiver Operating Characteristic (ROC) curves, which showed training and testing accuracies of 0.713 and 0.701, respectively. The GPZ map was classified into five classes such as very high, high, moderate, low, and very low. The study revealed that approximately 45% of the area falls under the moderate GPZ, while only 30% of the region is classified as having a high GPZ. The area receives high rainfall but has very high surface runoff due to no proper developed soil and lack of water conservation structures. Every summer season show a declined groundwater level. In this context, results of study area are useful to maintain the groundwater under climate change and summer season. The GPZ map plays an important role in implementing artificial recharge structures (ARS), such as percolation ponds, tube wells, bore wells, cement nala bunds (CNBs), continuous contour trenching (CCTs), and others for development of ground level. This study is significant for developing sustainable groundwater management policies in semi-arid regions, that are experiencing climate change. Proper groundwater potential mapping and watershed development policies can help mitigate the effects of drought, climate change, and water scarcity, while preserving the ecosystem in the Limestone, Shales, and Sandstone compact rock region. The results of this study are essential for farmers, regional planners, policy-makers, climate change experts, and local governments, enabling them to understand the groundwater development possibilities in the study area.
    Keywords aquifers ; bunds ; cement ; climate change ; drainage ; drought ; ecosystems ; geographic information systems ; groundwater ; groundwater recharge ; land cover ; land use ; limestone ; rain ; research ; runoff ; sandstone ; soil ; summer ; topography ; water conservation ; water shortages ; water table ; watersheds ; India ; LULC ; ROC ; AHP ; Hydrogeological ; Madhya Pradesh
    Language English
    Dates of publication 2023-07
    Publishing place Elsevier Inc.
    Document type Article ; Online
    Note Pre-press version
    ZDB-ID 205699-9
    ISSN 1096-0953 ; 0013-9351
    ISSN (online) 1096-0953
    ISSN 0013-9351
    DOI 10.1016/j.envres.2023.115832
    Database NAL-Catalogue (AGRICOLA)

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  9. Article: Prediction of soil chemical properties using multispectral satellite images and wavelet transforms methods

    Pande, Chaitanya B. / Kadam, Sunil A. / Jayaraman, Rajesh / Gorantiwar, Sunil / Shinde, Mukund

    Journal of the Saudi Society of Agricultural Sciences. 2022 Jan., v. 21, no. 1

    2022  

    Abstract: The main aim of this paper is to develop the accurately map of soil chemical parameters were used for development of the agriculture, forestry, ecological planning, and crop yield production. Soil chemical properties analysis and forecast model was ... ...

    Abstract The main aim of this paper is to develop the accurately map of soil chemical parameters were used for development of the agriculture, forestry, ecological planning, and crop yield production. Soil chemical properties analysis and forecast model was developed and validated with the Wavelet transform methods and multispectral satellite images. At the study area sites, satellite images and soil samples were collected during a similar time. Three most important soil chemical properties such as organic carbon, pH and EC were chosen to development of predication modeling based on the soil chemical information. This valuable information of parameters was analysed according to conventional methods. The observed data of soil was used for the predication modeling of soil chemical properties by MATLAB software. The identification of soil chemical properties was the subject of multi-spectral satellite images through algorithm of soil predication modeling. The real chemical characteristics of the soil are associated to wavelet transformation methods. Forecasting of soil chemical properties and this model can be given more accurate information related to soil nutrient parameters. Now a day’s machine learning programming is an easy to applied on the natural resources and agriculture studies. The chemical characteristics of the soil are compared with the different spectrum wavelengths of the MATLAB program. Therefore, four wavelets models like Daubechies, Symlet, Biorthogonal and Coiflet were selected to development of predication modelling, which wavelet model can be given more accurate information with best model of the soil chemical properties. Also, the coefficient of five key components and soil-chemical values were associated in the MATLAB software. In the semi-arid regions in, India, which components have been highly correlated with soil parameters in the predicated modeling. More detailed information of soil chemical characteristics was provided by four selected wavelet models developed based on the observed data and satellite data. Prediction of soil chemical values has been identified through low and high-frequency satellite images and artificial neural network model. In this study, the neural network wavelet model was used to predicted values related to soil chemical properties in the semi-arid region. The developed two models, like polynomial and ANN, have been validated and compared to the soil chemical properties data, which models can be fitted with the study of soil chemical properties. The results of the study area can be more beneficial for development of agriculture activities, climate change approaches, crop and soil suitability planning. From the results of models have been given a fast and quickly information of soil nutrient parameters without laboratory analysis. The results of predicated values can be more helpful to precision farming related activates and soil fertility mapping to provide the farmers and agriculture scientist.
    Keywords algorithms ; climate change ; computer software ; crop yield ; forestry ; neural networks ; organic carbon ; pH ; prediction ; remote sensing ; satellites ; scientists ; semiarid zones ; soil fertility ; soil nutrients ; soil suitability ; wavelet ; India
    Language English
    Dates of publication 2022-01
    Size p. 21-28.
    Publishing place Elsevier B.V.
    Document type Article
    ZDB-ID 2635379-9
    ISSN 1658-077X
    ISSN 1658-077X
    DOI 10.1016/j.jssas.2021.06.016
    Database NAL-Catalogue (AGRICOLA)

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  10. Article: Prediction of irrigation groundwater quality parameters using ANN, LSTM, and MLR models

    Kouadri, Saber / Pande, Chaitanya B. / Panneerselvam, Balamurugan / Moharir, Kanak N. / Elbeltagi, Ahmed

    Environmental science and pollution research. 2022 Mar., v. 29, no. 14

    2022  

    Abstract: Forecasting the irrigation groundwater parameters helps plan irrigation water and crop, and it is commonly expensive because it needs various parameters, mainly in developing nations. Therefore, the present research’s core objective is to create accurate ...

    Abstract Forecasting the irrigation groundwater parameters helps plan irrigation water and crop, and it is commonly expensive because it needs various parameters, mainly in developing nations. Therefore, the present research’s core objective is to create accurate and reliable machine learning models for irrigation parameters. To accomplish this determination, three machine learning (ML) models, viz. long short-term memory (LSTM), multi-linear regression (MLR), and artificial neural network (ANN), have been trained. It is validated with mean squared error (MSE) and correlation coefficients (r), root mean square error (RMSE), and mean absolute error (MAE). These machine learning models have been used and applied for predicating the six irrigation water quality parameters such as sodium absorption ratio (SAR), percentage of sodium (%Na), residual sodium carbonate (RSC), magnesium hazard (MH), Permeability Index (PI), and Kelly ratio (KR). Therefore, the two scenario performances of ANN, LSTM, and MLR have been developed for each model to predict irrigation water quality parameters. The first and second scenario performance was created based on all and second reduction input variables. The ANN, LSTM, and MLR models have discovered that excluding for ANN and MLR models shows high accuracy in first and second scenario models, respectively. These model’s accuracy was checked based on the mean squared error (MSE), correlation coefficients (r), and root mean square error (RMSE) for training and testing processes serially. The RSC values are highly accurate predicated values using ANN and MLR models. As a result, machine learning models may improve irrigation water quality parameters, and such types of results are essential to farmers and crop planning in various irrigation processes.
    Keywords absorption ; groundwater ; irrigation ; irrigation water ; magnesium ; neural networks ; permeability ; pollution ; prediction ; research ; sodium ; sodium carbonate ; water quality
    Language English
    Dates of publication 2022-03
    Size p. 21067-21091.
    Publishing place Springer Berlin Heidelberg
    Document type Article
    ZDB-ID 1178791-0
    ISSN 1614-7499 ; 0944-1344
    ISSN (online) 1614-7499
    ISSN 0944-1344
    DOI 10.1007/s11356-021-17084-3
    Database NAL-Catalogue (AGRICOLA)

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