Addressing water scarcity challenges through rainwater harvesting: A comprehensive analysis of potential zones and model performance in arid and semi-arid regions–A case study on Purulia, India
Water scarcity in arid and semi-arid regions is a critical global concern, necessitating innovative solutions to address increasing water demands in these vulnerable areas. This study focuses on tackling this challenge by identifying and classifying rainwater harvesting zones based on their potentia...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2024-01-01
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| Series: | HydroResearch |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589757824000118 |
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| author | Subhra Halder Suddhasil Bose |
| author_facet | Subhra Halder Suddhasil Bose |
| author_sort | Subhra Halder |
| collection | DOAJ |
| description | Water scarcity in arid and semi-arid regions is a critical global concern, necessitating innovative solutions to address increasing water demands in these vulnerable areas. This study focuses on tackling this challenge by identifying and classifying rainwater harvesting zones based on their potentiality and comparing the performance of two machine learning models, Artificial Neural Network (ANN) and Random Forest (RF), for optimizing rainwater harvesting strategies. The study area is Purulia, a district in India. Extensive literature review was conducted to identify key factors influencing rainwater harvesting. Open-source remotely sensed data were employed to pinpoint rainwater harvesting potential zones. A multi-criteria decision-making technique was applied to assess the importance of various factors. Results indicated that rainfall, slope, runoff potential, soil, land cover, and drainage density are the six crucial factors for selecting suitable rainwater harvesting locations. Approximately 2% of the area is unsuitable, 8% is poorly suitable, 33% is moderately suitable, 45% is highly suitable, and the remaining 12% is extremely suitable in Purulia. Two predictive models were developed, with the RF algorithm demonstrating nearly 99% accuracy. Finally, remedial techniques for mitigating water scarcity through rainwater harvesting are discussed separately for urban and rural areas. This research article embraces a comprehensive approach to address water-related concerns, offering a replicable framework applicable globally, with a specific focus on arid and semi-arid regions. |
| format | Article |
| id | doaj-art-48e352d098be4d3090c5d0d9da9c2e21 |
| institution | Kabale University |
| issn | 2589-7578 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | HydroResearch |
| spelling | doaj-art-48e352d098be4d3090c5d0d9da9c2e212024-11-29T06:24:52ZengKeAi Communications Co., Ltd.HydroResearch2589-75782024-01-017201212Addressing water scarcity challenges through rainwater harvesting: A comprehensive analysis of potential zones and model performance in arid and semi-arid regions–A case study on Purulia, IndiaSubhra Halder0Suddhasil Bose1School of Water Resources Engineering, Jadavpur University,Kolkata 700032, West Bengal, IndiaCorresponding author.; School of Water Resources Engineering, Jadavpur University,Kolkata 700032, West Bengal, IndiaWater scarcity in arid and semi-arid regions is a critical global concern, necessitating innovative solutions to address increasing water demands in these vulnerable areas. This study focuses on tackling this challenge by identifying and classifying rainwater harvesting zones based on their potentiality and comparing the performance of two machine learning models, Artificial Neural Network (ANN) and Random Forest (RF), for optimizing rainwater harvesting strategies. The study area is Purulia, a district in India. Extensive literature review was conducted to identify key factors influencing rainwater harvesting. Open-source remotely sensed data were employed to pinpoint rainwater harvesting potential zones. A multi-criteria decision-making technique was applied to assess the importance of various factors. Results indicated that rainfall, slope, runoff potential, soil, land cover, and drainage density are the six crucial factors for selecting suitable rainwater harvesting locations. Approximately 2% of the area is unsuitable, 8% is poorly suitable, 33% is moderately suitable, 45% is highly suitable, and the remaining 12% is extremely suitable in Purulia. Two predictive models were developed, with the RF algorithm demonstrating nearly 99% accuracy. Finally, remedial techniques for mitigating water scarcity through rainwater harvesting are discussed separately for urban and rural areas. This research article embraces a comprehensive approach to address water-related concerns, offering a replicable framework applicable globally, with a specific focus on arid and semi-arid regions.http://www.sciencedirect.com/science/article/pii/S2589757824000118Rainwater harvestingMulti criteria decision makingGeographical information systemMachine learning modelPurulia |
| spellingShingle | Subhra Halder Suddhasil Bose Addressing water scarcity challenges through rainwater harvesting: A comprehensive analysis of potential zones and model performance in arid and semi-arid regions–A case study on Purulia, India HydroResearch Rainwater harvesting Multi criteria decision making Geographical information system Machine learning model Purulia |
| title | Addressing water scarcity challenges through rainwater harvesting: A comprehensive analysis of potential zones and model performance in arid and semi-arid regions–A case study on Purulia, India |
| title_full | Addressing water scarcity challenges through rainwater harvesting: A comprehensive analysis of potential zones and model performance in arid and semi-arid regions–A case study on Purulia, India |
| title_fullStr | Addressing water scarcity challenges through rainwater harvesting: A comprehensive analysis of potential zones and model performance in arid and semi-arid regions–A case study on Purulia, India |
| title_full_unstemmed | Addressing water scarcity challenges through rainwater harvesting: A comprehensive analysis of potential zones and model performance in arid and semi-arid regions–A case study on Purulia, India |
| title_short | Addressing water scarcity challenges through rainwater harvesting: A comprehensive analysis of potential zones and model performance in arid and semi-arid regions–A case study on Purulia, India |
| title_sort | addressing water scarcity challenges through rainwater harvesting a comprehensive analysis of potential zones and model performance in arid and semi arid regions a case study on purulia india |
| topic | Rainwater harvesting Multi criteria decision making Geographical information system Machine learning model Purulia |
| url | http://www.sciencedirect.com/science/article/pii/S2589757824000118 |
| work_keys_str_mv | AT subhrahalder addressingwaterscarcitychallengesthroughrainwaterharvestingacomprehensiveanalysisofpotentialzonesandmodelperformanceinaridandsemiaridregionsacasestudyonpuruliaindia AT suddhasilbose addressingwaterscarcitychallengesthroughrainwaterharvestingacomprehensiveanalysisofpotentialzonesandmodelperformanceinaridandsemiaridregionsacasestudyonpuruliaindia |