Your search found 4 records
1 Arabameri, A.; Pal, S. C.; Rezaie, F.; Nalivan, O. A.; Chowdhuri, I.; Saha, A.; Lee, S.; Moayedi, H. 2021. Modeling groundwater potential using novel GIS-based machine-learning ensemble techniques. Journal of Hydrology: Regional Studies, 36:100848. [doi: https://doi.org/10.1016/j.ejrh.2021.100848]
Groundwater potential ; Modelling ; Geographical information systems ; Machine learning ; Techniques ; Neural networks ; Remote sensing ; River basins ; Land use ; Land cover ; Landslides / Iran (Islamic Republic of) / Tabriz River Basin
(Location: IWMI HQ Call no: e-copy only Record No: H050645)
https://www.sciencedirect.com/science/article/pii/S221458182100077X/pdfft?md5=008d5c28c1313c1b111fb09896b85615&pid=1-s2.0-S221458182100077X-main.pdf
https://vlibrary.iwmi.org/pdf/H050645.pdf
https://vlibrary.iwmi.org/pdf/H050645.pdf
(14.10 MB) (14.1 MB)
Study region: The present study has been carried out in the Tabriz River basin (5397 km2) in north-western Iran. Elevations vary from 1274 to 3678 m above sea level, and slope angles range from 0 to 150.9 %. The average annual minimum and maximum temperatures are 2 °C and 12 °C, respectively. The average annual rainfall ranges from 243 to 641 mm, and the northern and southern parts of the basin receive the highest amounts.
Study focus: In this study, we mapped the groundwater potential (GWP) with a new hybrid model combining random subspace (RS) with the multilayer perception (MLP), naïve Bayes tree (NBTree), and classification and regression tree (CART) algorithms. A total of 205 spring locations were collected by integrating field surveys with data from Iran Water Resources Management, and divided into 70:30 for training and validation. Fourteen groundwater conditioning factors (GWCFs) were used as independent model inputs. Statistics such as receiver operating characteristic (ROC) and five others were used to evaluate the performance of the models.
New hydrological insights for the region: The results show that all models performed well for GWP mapping (AUC > 0.8). The hybrid MLP-RS model achieved high validation scores (AUC = 0.935). The relative importance of GWCFs was revealed that slope, elevation, TRI and HAND are the most important predictors of groundwater presence. This study demonstrates that hybrid ensemble models can support sustainable management of groundwater resources.
Study focus: In this study, we mapped the groundwater potential (GWP) with a new hybrid model combining random subspace (RS) with the multilayer perception (MLP), naïve Bayes tree (NBTree), and classification and regression tree (CART) algorithms. A total of 205 spring locations were collected by integrating field surveys with data from Iran Water Resources Management, and divided into 70:30 for training and validation. Fourteen groundwater conditioning factors (GWCFs) were used as independent model inputs. Statistics such as receiver operating characteristic (ROC) and five others were used to evaluate the performance of the models.
New hydrological insights for the region: The results show that all models performed well for GWP mapping (AUC > 0.8). The hybrid MLP-RS model achieved high validation scores (AUC = 0.935). The relative importance of GWCFs was revealed that slope, elevation, TRI and HAND are the most important predictors of groundwater presence. This study demonstrates that hybrid ensemble models can support sustainable management of groundwater resources.
2 Bharucha, Z. P.; Attwood, S.; Badiger, S.; Balamatti, A.; Bawden, R.; Bentley, J. W.; Chander, M.; Davies, L.; Dixon, H.; Dixon, J.; D’Souza, M.; Flora, C. B.; Green, M.; Joshi, D.; Komarek, A. M.; McDermid, L. R.; Mathijs, E.; Rola, A. C.; Patnaik, S.; Pattanayak, S.; Pingali, P.; Prasad, V. P. V.; Rabbinge, R.; Ramanjaneyulu, G. V.; Ravindranath, N. H.; Sage, C.; Saha, A.; Salvatore, C.; Saxena, L. P.; Singh, C.; Smith, P.; Srinidhi, A.; Sugam, R.; Thomas, R.; Uphoff, N.; Pretty, J. 2021. The top 100 questions for the sustainable intensification of agriculture in India’s rainfed drylands. International Journal of Agricultural Sustainability, 19(2):106-127. [doi: https://doi.org/10.1080/14735903.2020.1830530]
Sustainable intensification ; Rainfed agriculture ; Dryland farming ; Agricultural development ; Policies ; Farming systems ; Agricultural production ; Livestock ; Climate change ; Resilience ; Ecosystem services ; Natural resources ; Water resources ; Watersheds / India
(Location: IWMI HQ Call no: e-copy only Record No: H051091)
(2.04 MB)
India has the largest area of rainfed dryland agriculture globally, with a variety of distinct types of farming systems producing most of its coarse cereals, food legumes, minor millets, and large amounts of livestock. All these are vital for national and regional food and nutritional security. Yet, the rainfed drylands have been relatively neglected in mainstream agricultural and rural development policy. As a result, significant social-ecological challenges overlap in these landscapes: endemic poverty, malnutrition and land degradation. Sustainable intensification of dryland agriculture is essential for helping to address these challenges, particularly in the context of accelerating climate change. In this paper, we present 100 questions that point to the most important knowledge gaps and research priorities. If addressed, these would facilitate and inform sustainable intensification in Indian rainfed drylands, leading to improved agricultural production and enhanced ecosystem services. The horizon scanning method used to produce these questions brought together experts and practitioners involved in a broad range of disciplines and sectors. This exercise resulted in a consolidated set of questions covering the agricultural drylands, organized into 13 themes. Together, these represent a collective programme for new cross- and multi-disciplinary research on sustainable intensification in the Indian rainfed drylands.
3 Hoover, D. L.; Abendroth, L. J.; Browning, D. M.; Saha, A.; Snyder, K.; Wagle, P.; Witthaus, L.; Baffaut, C.; Biederman, J. A.; Bosch, D. D.; Bracho, R.; Busch, D.; Clark, P.; Ellsworth, P.; Fay, P. A.; Flerchinger, G.; Kearney, S.; Levers, L.; Saliendra, N.; Schmer, M.; Schomberg, H.; Scott, R. L. 2023. Indicators of water use efficiency across diverse agroecosystems and spatiotemporal scales. Science of the Total Environment, 864:160992. (Online first) [doi: https://doi.org/10.1016/j.scitotenv.2022.160992]
Agroecosystems ; Water use efficiency ; Indicators ; Biomass ; Climate change ; Agricultural production ; Environmental impact ; Ecosystems ; Vegetation ; Farmland ; Water productivity ; Rangelands ; Transpiration ; Food production
(Location: IWMI HQ Call no: e-copy only Record No: H051688)
https://www.sciencedirect.com/science/article/pii/S0048969722080950/pdfft?md5=f61fd20085042b555930315b46212634&pid=1-s2.0-S0048969722080950-main.pdf
https://vlibrary.iwmi.org/pdf/H051688.pdf
https://vlibrary.iwmi.org/pdf/H051688.pdf
(3.57 MB) (3.57 MB)
Understanding the relationship between water and production within and across agroecosystems is essential for addressing several agricultural challenges of the 21st century: providing food, fuel, and fiber to a growing human population, reducing the environmental impacts of agricultural production, and adapting food systems to climate change. Of all human activities, agriculture has the highest demand for water globally. Therefore, increasing water use efficiency (WUE), or producing ‘more crop per drop’, has been a long-term goal of agricultural management, engineering, and crop breeding. WUE is a widely used term applied across a diverse array of spatial scales, spanning from the leaf to the globe, and over temporal scales ranging from seconds to months to years. The measurement, interpretation, and complexity of WUE varies enormously across these spatial and temporal scales, challenging comparisons within and across diverse agroecosystems. The goals of this review are to evaluate common indicators of WUE in agricultural production and assess tradeoffs when applying these indicators within and across agroecosystems amidst a changing climate. We examine three questions: (1) what are the uses and limitations of common WUE indicators, (2) how can WUE indicators be applied within and across agroecosystems, and (3) how can WUE indicators help adapt agriculture to climate change? Addressing these agricultural challenges will require land managers, producers, policy makers, researchers, and consumers to evaluate costs and benefits of practices and innovations of water use in agricultural production. Clearly defining and interpreting WUE in the most scale-appropriate way is crucial for advancing agroecosystem sustainability.
4 Biswas, T.; Pal, S. C.; Chowdhuri, I.; Ruidas, D.; Saha, A.; Islam, A. R. Md. T.; Shit, M. 2023. Effects of elevated arsenic and nitrate concentrations on groundwater resources in Deltaic Region of Sundarban Ramsar Site, Indo-Bangladesh Region. Marine Pollution Bulletin, 188:114618. (Online first) [doi: https://doi.org/10.1016/j.marpolbul.2023.114618]
Groundwater ; Water resources ; Arsenic ; Nitrates ; Health hazards ; Drinking water ; Water quality ; Vulnerability ; Models / India / Bangladesh / Sundarban Ramsar Site
(Location: IWMI HQ Call no: e-copy only Record No: H051695)
(15.70 MB)
An attempt has been adopted to predict the As and NO3- concentration in groundwater (GW) in fast-growing coastal Ramsar region in eastern India. This study is focused to evaluate the As and NO3- vulnerable areas of coastal belts of the Indo-Bangladesh Ramsar site a hydro-geostrategic region of the world by using advanced ensemble ML techniques including NB-RF, NB-SVM and NB-Bagging. A total of 199 samples were collected from the entire study area for utilizing the 12 GWQ conditioning factors. The predicted results are certified that NB-Bagging the most suitable and preferable model in this current research. The vulnerability of As and NO3- concentration shows that most of the areas are highly vulnerable to As and low to moderately vulnerable to NO3. The reliable findings of this present study will help the management authorities and policymakers in taking preventive measures in reducing the vulnerability of water resources and corresponding health risks.
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