An Artificial Neural Network Model for Water Quality Prediction in the Amoju Hydrographic Subbasin, Cajamarca-Peru
Water quality is crucial for sustaining life, and accurate prediction models are essential for effective management. This study introduces an Artificial Neural Network (ANN) model designed to predict the Water Quality Index (WQI) in the Amoju Hydrographic Subbasin, Cajamarca-Peru. The model was deve...
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| Veröffentlicht in: | International journal of advanced computer science & applications 2024-01, Vol.15 (9) |
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| creator | Llanos, Alex Alfredo Huaman Meza, Jeimis Royler Yalta Cordova, Danicza Violeta Sanchez Martinez, Juan Carlos Chasquero Huatangari, Lenin Quiñones Sanchez, Dulcet Lorena Quinto Segura, Roxana Rojas Gutierrez, Alfredo Lazaro Ludeña |
| description | Water quality is crucial for sustaining life, and accurate prediction models are essential for effective management. This study introduces an Artificial Neural Network (ANN) model designed to predict the Water Quality Index (WQI) in the Amoju Hydrographic Subbasin, Cajamarca-Peru. The model was developed using key water quality parameters, including electrical conductivity (EC), total dissolved solids (TDS), calcium carbonate (CaCO3), and phosphate (〖PO〗_4^(3-)), identified through Pearson correlation analysis. Data from water samples collected over six months were used to train and validate the model. Results revealed that the ANN model achieved high predictive accuracy, with a significant correlation between WQI and the aforementioned parameters. The model's performance outstrips traditional methods demonstrating its capability to effectively capture complex interdependencies among water quality indicators. This research emphasizes the potential of AI-driven approaches for enhancing predictive accuracy in environmental monitoring. Future studies should consider incorporating additional variables, such as heavy metals and microbial indicators, and consider the application of real-time AI-driven monitoring systems to further refine water quality management strategies. The ANN model presented here offers a promising tool for decision-makers, providing a reliable method for predicting water quality in similar hydrographic basins and contributing to the broader field of AI in environmental science. |
| doi_str_mv | 10.14569/IJACSA.2024.01509104 |
| format | Article |
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This study introduces an Artificial Neural Network (ANN) model designed to predict the Water Quality Index (WQI) in the Amoju Hydrographic Subbasin, Cajamarca-Peru. The model was developed using key water quality parameters, including electrical conductivity (EC), total dissolved solids (TDS), calcium carbonate (CaCO3), and phosphate (〖PO〗_4^(3-)), identified through Pearson correlation analysis. Data from water samples collected over six months were used to train and validate the model. Results revealed that the ANN model achieved high predictive accuracy, with a significant correlation between WQI and the aforementioned parameters. The model's performance outstrips traditional methods demonstrating its capability to effectively capture complex interdependencies among water quality indicators. This research emphasizes the potential of AI-driven approaches for enhancing predictive accuracy in environmental monitoring. Future studies should consider incorporating additional variables, such as heavy metals and microbial indicators, and consider the application of real-time AI-driven monitoring systems to further refine water quality management strategies. The ANN model presented here offers a promising tool for decision-makers, providing a reliable method for predicting water quality in similar hydrographic basins and contributing to the broader field of AI in environmental science.</description><identifier>ISSN: 2158-107X</identifier><identifier>EISSN: 2156-5570</identifier><identifier>DOI: 10.14569/IJACSA.2024.01509104</identifier><language>eng</language><publisher>West Yorkshire: Science and Information (SAI) Organization Limited</publisher><subject>Accuracy ; Artificial intelligence ; Artificial neural networks ; Calcium carbonate ; Classification ; Complex variables ; Computer science ; Correlation analysis ; Deep learning ; Dissolved solids ; Electrical resistivity ; Environmental management ; Environmental monitoring ; Heavy metals ; Indicators ; Internet of Things ; Machine learning ; Microorganisms ; Neural networks ; Parameter identification ; Performance prediction ; Prediction models ; Public health ; Quality management ; Real time ; Real variables ; Support vector machines ; Water quality ; Water resources management ; Water sampling ; Wavelet transforms</subject><ispartof>International journal of advanced computer science & applications, 2024-01, Vol.15 (9)</ispartof><rights>2024. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). 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This study introduces an Artificial Neural Network (ANN) model designed to predict the Water Quality Index (WQI) in the Amoju Hydrographic Subbasin, Cajamarca-Peru. The model was developed using key water quality parameters, including electrical conductivity (EC), total dissolved solids (TDS), calcium carbonate (CaCO3), and phosphate (〖PO〗_4^(3-)), identified through Pearson correlation analysis. Data from water samples collected over six months were used to train and validate the model. Results revealed that the ANN model achieved high predictive accuracy, with a significant correlation between WQI and the aforementioned parameters. The model's performance outstrips traditional methods demonstrating its capability to effectively capture complex interdependencies among water quality indicators. This research emphasizes the potential of AI-driven approaches for enhancing predictive accuracy in environmental monitoring. Future studies should consider incorporating additional variables, such as heavy metals and microbial indicators, and consider the application of real-time AI-driven monitoring systems to further refine water quality management strategies. The ANN model presented here offers a promising tool for decision-makers, providing a reliable method for predicting water quality in similar hydrographic basins and contributing to the broader field of AI in environmental science.</description><subject>Accuracy</subject><subject>Artificial intelligence</subject><subject>Artificial neural networks</subject><subject>Calcium carbonate</subject><subject>Classification</subject><subject>Complex variables</subject><subject>Computer science</subject><subject>Correlation analysis</subject><subject>Deep learning</subject><subject>Dissolved solids</subject><subject>Electrical resistivity</subject><subject>Environmental management</subject><subject>Environmental monitoring</subject><subject>Heavy metals</subject><subject>Indicators</subject><subject>Internet of Things</subject><subject>Machine learning</subject><subject>Microorganisms</subject><subject>Neural networks</subject><subject>Parameter identification</subject><subject>Performance prediction</subject><subject>Prediction models</subject><subject>Public health</subject><subject>Quality management</subject><subject>Real time</subject><subject>Real variables</subject><subject>Support vector machines</subject><subject>Water quality</subject><subject>Water resources management</subject><subject>Water sampling</subject><subject>Wavelet transforms</subject><issn>2158-107X</issn><issn>2156-5570</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>8G5</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNo1kEtLw0AUhYMoWLQ_QRhwa-rcebVZhqC2UrVSRXdhMg87sc3UyQTpvze2ejfnLg7nHL4kuQA8AsZFdj27z4tlPiKYsBEGjjPA7CgZEOAi5XyMj_f_JAU8fj9Nhm1b4_5oRsSEDpKQNygP0VmnnFyjR9OFvcRvHz7Rg9dmjawP6E1GE9BzJ9cu7tAiGO1UdL5BrkFxZVC-8XWHpjsd_EeQ25VTaNlVlWxdc4UKWcuNDEqmCxO68-TEynVrhn96lrze3rwU03T-dDcr8nmqCBYx5RiPAWymdMUMEG6MxVIxhq3MMJlURErKAKjIuNYaNAiqGSPGkKySvLL0LLk85G6D_-pMG8vad6HpK0sKQATPBPDexQ8uFXzbBmPLbXD92F0JuNwTLg-Ey1_C5T9h-gOiTG8y</recordid><startdate>20240101</startdate><enddate>20240101</enddate><creator>Llanos, Alex Alfredo Huaman</creator><creator>Meza, Jeimis Royler Yalta</creator><creator>Cordova, Danicza Violeta Sanchez</creator><creator>Martinez, Juan Carlos Chasquero</creator><creator>Huatangari, Lenin Quiñones</creator><creator>Sanchez, Dulcet Lorena Quinto</creator><creator>Segura, Roxana Rojas</creator><creator>Gutierrez, Alfredo Lazaro Ludeña</creator><general>Science and Information (SAI) Organization Limited</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7XB</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>8G5</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>M2O</scope><scope>MBDVC</scope><scope>P5Z</scope><scope>P62</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope></search><sort><creationdate>20240101</creationdate><title>An Artificial Neural Network Model for Water Quality Prediction in the Amoju Hydrographic Subbasin, Cajamarca-Peru</title><author>Llanos, Alex Alfredo Huaman ; Meza, Jeimis Royler Yalta ; Cordova, Danicza Violeta Sanchez ; Martinez, Juan Carlos Chasquero ; Huatangari, Lenin Quiñones ; Sanchez, Dulcet Lorena Quinto ; Segura, Roxana Rojas ; Gutierrez, Alfredo Lazaro Ludeña</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c206t-500711f9cdb4e125eef0ac440fa9028b2aa34113695ddd1d163d442ee29ba5bf3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Accuracy</topic><topic>Artificial intelligence</topic><topic>Artificial neural networks</topic><topic>Calcium carbonate</topic><topic>Classification</topic><topic>Complex variables</topic><topic>Computer science</topic><topic>Correlation analysis</topic><topic>Deep learning</topic><topic>Dissolved solids</topic><topic>Electrical resistivity</topic><topic>Environmental management</topic><topic>Environmental monitoring</topic><topic>Heavy metals</topic><topic>Indicators</topic><topic>Internet of Things</topic><topic>Machine learning</topic><topic>Microorganisms</topic><topic>Neural networks</topic><topic>Parameter identification</topic><topic>Performance prediction</topic><topic>Prediction models</topic><topic>Public health</topic><topic>Quality management</topic><topic>Real time</topic><topic>Real variables</topic><topic>Support vector machines</topic><topic>Water quality</topic><topic>Water resources management</topic><topic>Water sampling</topic><topic>Wavelet transforms</topic><toplevel>online_resources</toplevel><creatorcontrib>Llanos, Alex Alfredo Huaman</creatorcontrib><creatorcontrib>Meza, Jeimis Royler Yalta</creatorcontrib><creatorcontrib>Cordova, Danicza Violeta Sanchez</creatorcontrib><creatorcontrib>Martinez, Juan Carlos Chasquero</creatorcontrib><creatorcontrib>Huatangari, Lenin Quiñones</creatorcontrib><creatorcontrib>Sanchez, Dulcet Lorena Quinto</creatorcontrib><creatorcontrib>Segura, Roxana Rojas</creatorcontrib><creatorcontrib>Gutierrez, Alfredo Lazaro Ludeña</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Research Library (Alumni Edition)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>Research Library</collection><collection>Research Library (Corporate)</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>ProQuest Central Basic</collection><jtitle>International journal of advanced computer science & applications</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Llanos, Alex Alfredo Huaman</au><au>Meza, Jeimis Royler Yalta</au><au>Cordova, Danicza Violeta Sanchez</au><au>Martinez, Juan Carlos Chasquero</au><au>Huatangari, Lenin Quiñones</au><au>Sanchez, Dulcet Lorena Quinto</au><au>Segura, Roxana Rojas</au><au>Gutierrez, Alfredo Lazaro Ludeña</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>An Artificial Neural Network Model for Water Quality Prediction in the Amoju Hydrographic Subbasin, Cajamarca-Peru</atitle><jtitle>International journal of advanced computer science & applications</jtitle><date>2024-01-01</date><risdate>2024</risdate><volume>15</volume><issue>9</issue><issn>2158-107X</issn><eissn>2156-5570</eissn><abstract>Water quality is crucial for sustaining life, and accurate prediction models are essential for effective management. This study introduces an Artificial Neural Network (ANN) model designed to predict the Water Quality Index (WQI) in the Amoju Hydrographic Subbasin, Cajamarca-Peru. The model was developed using key water quality parameters, including electrical conductivity (EC), total dissolved solids (TDS), calcium carbonate (CaCO3), and phosphate (〖PO〗_4^(3-)), identified through Pearson correlation analysis. Data from water samples collected over six months were used to train and validate the model. Results revealed that the ANN model achieved high predictive accuracy, with a significant correlation between WQI and the aforementioned parameters. The model's performance outstrips traditional methods demonstrating its capability to effectively capture complex interdependencies among water quality indicators. This research emphasizes the potential of AI-driven approaches for enhancing predictive accuracy in environmental monitoring. Future studies should consider incorporating additional variables, such as heavy metals and microbial indicators, and consider the application of real-time AI-driven monitoring systems to further refine water quality management strategies. The ANN model presented here offers a promising tool for decision-makers, providing a reliable method for predicting water quality in similar hydrographic basins and contributing to the broader field of AI in environmental science.</abstract><cop>West Yorkshire</cop><pub>Science and Information (SAI) Organization Limited</pub><doi>10.14569/IJACSA.2024.01509104</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Accuracy Artificial intelligence Artificial neural networks Calcium carbonate Classification Complex variables Computer science Correlation analysis Deep learning Dissolved solids Electrical resistivity Environmental management Environmental monitoring Heavy metals Indicators Internet of Things Machine learning Microorganisms Neural networks Parameter identification Performance prediction Prediction models Public health Quality management Real time Real variables Support vector machines Water quality Water resources management Water sampling Wavelet transforms |
| title | An Artificial Neural Network Model for Water Quality Prediction in the Amoju Hydrographic Subbasin, Cajamarca-Peru |
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