Your search found 8 records
1 Lall, U. 2009. Managing climate risks for water resources in a changing environment. In Chartres, Colin (Ed.). Words into action: delegate publication for the 5th World Water Forum, Istanbul, Turkey, 16-22 March 2009. London, UK: Faircount Media Group. pp.98-104.
Climate ; Risks ; Assessment ; Models ; Climate change ; Water resource management ; Hydrology ; Rain ; Prediction ; Water allocation ; Flood control ; Crop management
(Location: IWMI HQ Call no: IWMI 333.9162 G635 SAL Record No: H042192)
(1.34 MB)
2 Phoobane, P.; Masinde, M.; Mabhaudhi, Tafadzwanashe. 2022. Predicting infectious diseases: a bibliometric review on Africa. International Journal of Environmental Research and Public Health, 19(3):1893. [doi: https://doi.org/10.3390/ijerph19031893]
Infectious diseases ; Prediction ; Bibliometric analysis ; Machine learning ; Artificial intelligence ; Malaria ; COVID-19 ; Ebola virus disease ; Collaboration ; Models / Africa
(Location: IWMI HQ Call no: e-copy only Record No: H050967)
(35.50 MB) (35.5 MB)
Africa has a long history of novel and re-emerging infectious disease outbreaks. This reality has attracted the attention of researchers interested in the general research theme of predicting infectious diseases. However, a knowledge mapping analysis of literature to reveal the research trends, gaps, and hotspots in predicting Africa’s infectious diseases using bibliometric tools has not been conducted. A bibliometric analysis of 247 published papers on predicting infectious diseases in Africa, published in the Web of Science core collection databases, is presented in this study. The results indicate that the severe outbreaks of infectious diseases in Africa have increased scientific publications during the past decade. The results also reveal that African researchers are highly underrepresented in these publications and that the United States of America (USA) is the most productive and collaborative country. The relevant hotspots in this research field include malaria, models, classification, associations, COVID-19, and cost-effectiveness. Furthermore, weather-based prediction using meteorological factors is an emerging theme, and very few studies have used the fourth industrial revolution (4IR) technologies. Therefore, there is a need to explore 4IR predicting tools such as machine learning and consider integrated approaches that are pivotal to developing robust prediction systems for infectious diseases, especially in Africa. This review paper provides a useful resource for researchers, practitioners, and research funding agencies interested in the research theme—the prediction of infectious diseases in Africa—by capturing the current research hotspots and trends.
3 Bangira, T.; Mutanga, O.; Sibanda, M.; Dube, T.; Mabhaudhi, Tafadzwanashe. 2023. Remote sensing grassland productivity attributes: a systematic review. Remote Sensing, 15(8):2043. [doi: https://doi.org/10.3390/rs15082043]
Grasslands ; Productivity ; Prediction ; Remote sensing ; Estimation ; Monitoring ; Techniques ; Ecosystem services ; Leaf area index ; Above ground biomass ; Canopy ; Chlorophylls ; Nitrogen content ; Vegetation index
(Location: IWMI HQ Call no: e-copy only Record No: H051841)
https://www.mdpi.com/2072-4292/15/8/2043/pdf?version=1681347101
https://vlibrary.iwmi.org/pdf/H051841.pdf
https://vlibrary.iwmi.org/pdf/H051841.pdf
(3.26 MB) (3.26 MB)
A third of the land on the Earth is composed of grasslands, mainly used for forage. Much effort is being conducted to develop tools to estimate grassland productivity (GP) at different extents, concentrating on spatial and seasonal variability pertaining to climate change. GP is a reliable indicator of how well an ecosystem works because of its close connection to the ecological system equilibrium. The most commonly used proxies of GP in ecological studies are aboveground biomass (AGB), leaf area index (LAI), canopy storage capacity (CSC), and chlorophyll and nitrogen content. Grassland science gains much information from the capacity of remote sensing (RS) techniques to calculate GP proxies. An overview of the studies on RS-based GP prediction techniques and a discussion of current matters determining GP monitoring are critical for improving future GP prediction performance. A systematic review of articles published between 1970 and October 2021 (203 peer-reviewed articles from Web of Science, Scopus, and ScienceDirect databases) showed a trend in the choice of the sensors, and the approaches to use are largely dependent on the extent of monitoring and assessment. Notably, all the reviewed articles demonstrate the growing demand for high-resolution sensors, such as hyperspectral scanners and computationally efficient image-processing techniques for the high prediction accuracy of GP at various scales of application. Further research is required to attract the synthesis of optical and radar data, multi-sensor data, and the selection of appropriate techniques for GP prediction at different scales. Mastering and listing major uncertainties associated with different algorithms for the GP prediction and pledging to reduce these errors are critical.
4 Leggesse, E. S.; Zimale, F. A.; Sultan, D.; Enku, T.; Srinivasan, R.; Tilahun, Seifu A. 2023. Predicting optical water quality indicators from remote sensing using machine learning algorithms in tropical highlands of Ethiopia. Hydrology, 10(5):110. [doi: https://doi.org/10.3390/hydrology10050110]
Water quality ; Indicators ; Prediction ; Remote sensing ; Machine learning ; Algorithms ; Neural networks ; Modelling ; Total dissolved solids ; Turbidity ; Chlorophyll A ; Landsat ; Satellite imagery ; Monitoring ; Highlands ; Lakes / Ethiopia / Lake Tana
(Location: IWMI HQ Call no: e-copy only Record No: H051963)
https://www.mdpi.com/2306-5338/10/5/110/pdf?version=1684396571
https://vlibrary.iwmi.org/pdf/H051963.pdf
https://vlibrary.iwmi.org/pdf/H051963.pdf
(3.60 MB) (3.60 MB)
Water quality degradation of freshwater bodies is a concern worldwide, particularly in Africa, where data are scarce and standard water quality monitoring is expensive. This study explored the use of remote sensing imagery and machine learning (ML) algorithms as an alternative to standard field measuring for monitoring water quality in large and remote areas constrained by logistics and finance. Six machine learning (ML) algorithms integrated with Landsat 8 imagery were evaluated for their accuracy in predicting three optically active water quality indicators observed monthly in the period from August 2016 to April 2022: turbidity (TUR), total dissolved solids (TDS) and Chlorophyll a (Chl-a). The six ML algorithms studied were the artificial neural network (ANN), support vector machine regression (SVM), random forest regression (RF), XGBoost regression (XGB), AdaBoost regression (AB), and gradient boosting regression (GB) algorithms. XGB performed best at predicting Chl-a, with an R2 of 0.78, Nash–Sutcliffe efficiency (NSE) of 0.78, mean absolute relative error (MARE) of 0.082 and root mean squared error (RMSE) of 9.79 µg/L. RF performed best at predicting TDS (with an R2 of 0.79, NSE of 0.80, MARE of 0.082, and RMSE of 12.30 mg/L) and TUR (with an R2 of 0.80, NSE of 0.81, and MARE of 0.072 and RMSE of 7.82 NTU). The main challenges were data size, sampling frequency, and sampling resolution. To overcome the data limitation, we used a K-fold cross validation technique that could obtain the most out of the limited data to build a robust model. Furthermore, we also employed stratified sampling techniques to improve the ML modeling for turbidity. Thus, this study shows the possibility of monitoring water quality in large freshwater bodies with limited observed data using remote sensing integrated with ML algorithms, potentially enhancing decision making.
5 Pavelic, Paul; Villholth, K. G.; Verma, Shilp. (Eds.) 2023. Sustainable groundwater development for improved livelihoods in Sub-Saharan Africa. Abingdon, Oxon, UK: Routledge. 222p. (Routledge Special Issues on Water Policy and Governance)
Groundwater irrigation ; Groundwater potential ; Sustainability ; Livelihoods ; Water resources ; Water management ; Smallholders ; Farmers ; Small-scale irrigation ; Irrigated farming ; Rainfed farming ; Pumps ; Wells ; Boreholes ; Water availability ; Water balance ; Water use ; Groundwater recharge ; Aquifers ; Prediction ; Water table ; Groundwater extraction ; Water quality ; Hydrogeology ; Technology adoption ; Energy ; Institutions ; Water policies ; Water governance ; Gender ; Women ; Households ; Socioeconomic aspects ; Poverty ; Income ; Food security ; Land tenure ; Markets ; Investment ; Supply chains ; Cost benefit analysis ; Credit ; Financing ; Subsidies ; Evapotranspiration ; River basins ; Dry season ; Livestock / Africa South of Sahara / Burkina Faso / Ethiopia / Ghana / Kenya / Malawi / Mali / Mozambique / Niger / Nigeria / Rwanda / United Republic of Tanzania / Uganda / Zambia / Raya Valley / Kobo Valley / Dantiandou Valley / Volta River Basin / Iullemmeden Basin / Zalerigu / Sapeliga / Talensi-Nabdam District / Bawku West
(Location: IWMI HQ Call no: e-copy SF Record No: H052019)
(0.07 MB)
6 Anayah, F. M.; Kaluarachchi, J. J.; Pavelic, Paul; Smakhtin, V. 2023. Predicting groundwater recharge in Ghana by estimating evapotranspiration. In Pavelic, Paul; Villholth, K. G.; Verma, Shilp. (Eds.). Sustainable groundwater development for improved livelihoods in Sub-Saharan Africa. Abingdon, Oxon, UK: Routledge. pp.160-184. (Routledge Special Issues on Water Policy and Governance)
Groundwater recharge ; Prediction ; Evapotranspiration ; Estimation ; Water balance ; Water demand ; Water resources ; Spatial distribution ; Rainfall / Africa South of Sahara / Ghana / Volta River Basin
(Location: IWMI HQ Call no: e-copy SF Record No: H052028)
This study uses a modified Granger and Gray model to estimate evapotranspiration and then groundwater recharge in Ghana. The overall results show that the model is capable of reliably predicting regional evapotranspiration using a small number of monitoring stations with meteorological data only. This information allows the estimation of groundwater recharge via the water balance equation. The results indicate that the aquifer system is sufficiently recharged, especially in northern Ghana, where dry conditions prevail, to allow the development of groundwater resources to satisfy increasing water demands.
7 Ileperuma, Kaveesha; Jampani, Mahesh; Sellahewa, Uvindu; Panjwani, Shweta; Amarnath, Giriraj. 2023. Predicting malaria prevalence with machine learning models using satellite-based climate information: technical report. Colombo, Sri Lanka: International Water Management Institute (IWMI). CGIAR Initiative on Climate Resilience. 32p.
Malaria ; Prediction ; Machine learning ; Models ; Climatic data ; Satellite observation ; Rainfall patterns / Senegal
(Location: IWMI HQ Call no: e-copy only Record No: H052669)
(1.93 MB)
The current report presents a machine learning model developed to predict malaria prevalence based on rainfall patterns, specifically tailored to different regions within Senegal. The developed model takes into account the varying climate conditions across regions to provide a more localized and accurate prediction. The primary input parameters used for prediction include rainfall, month, and year, allowing the model to capture each region's seasonal variations and trends. This research aims to enhance the precision of malaria predictions, contributing to more effective and targeted public health measures. The model is designed to provide future forecasts, offering valuable insights into early warning signals to help anticipate and mitigate the impact of malaria outbreaks. This proactive approach enables authorities and healthcare professionals to prepare and implement preventive measures in advance, potentially reducing the severity of malaria-related issues and aiding in the allocation of resources where they are most needed. By tailoring the prediction model to the unique characteristics of each region in Senegal, the current research addresses the localized nature of malaria outbreaks, recognizing that factors such as climate, geography, and environmental conditions can significantly influence the prevalence of malaria. The integration of predictive analytics and models in public health initiatives allows for a more strategic and responsive approach to malaria management, ultimately contributing to the overall well-being of the affected communities. This report includes an explanation of the methodology used for the development of the prediction model, along with the results obtained and their implications for public health in Senegal.
8 Arheimer, B.; Cudennec, C.; Castellarin, A.; Grimaldi, S.; Heal, K. V.; Lupton, C.; Sarkar, A.; Tian, F.; Onema, J.-M. K.; Archfield, S.; Blöschl, G.; Chaffe, P. L. B.; Croke, B. F. W.; Dembélé, Moctar; Leong, C.; Mijic, A.; Mosquera, G. M.; Nlend, B.; Olusola, A. O.; Polo, M. J.; Sandells, M.; Sheffield, J.; van Hateren, T. C.; Shafiei, M.; Adla, S.; Agarwal, A.; Aguilar, C.; Andersson, J. C. M.; Andraos, C.; Andreu, A.; Avanzi, F.; Bart, R. R.; Bartosova, A.; Batelaan, O.; Bennett, J. C.; Bertola, M.; Bezak, N.; Boekee, J.; Bogaard, T.; Booij, M. J.; Brigode, P.; Buytaert, W.; Bziava, K.; Castelli, G.; Castro, C. V.; Ceperley, N. C.; Chidepudi, S. K. R.; Chiew, F. H. S.; Chun, K. P.; Dagnew, A. G.; Dekongmen, B. W.; del Jesus, M.; Dezetter, A.; do Nascimento Batista, J. A.; Doble, R. C.; Dogulu, N.; Eekhout, J. P. C.; Elçi, A.; Elenius, M.; Finger, D. C.; Fiori, A.; Fischer, S.; Förster, K.; Ganora, D.; Ellouze, E. G.; Ghoreishi, M.; Harvey, N.; Hrachowitz, M.; Jampani, Mahesh; Jaramillo, F.; Jongen, H. J.; Kareem, K. Y.; Khan, U. T.; Khatami, S.; Kingston, D. G.; Koren, G.; Krause, S.; Kreibich, H.; Lerat, J.; Liu, J.; de Brito, M. M.; Mahé, G.; Makurira, H.; Mazzoglio, P.; Merheb, M.; Mishra, A.; Mohammad, H.; Montanari, A.; Mujere, N.; Nabavi, E.; Nkwasa, A.; Alegria, M. E. O.; Orieschnig, C.; Ovcharuk, V.; Palmate, S. S.; Pande, S.; Pandey, S.; Papacharalampous, G.; Pechlivanidis, I.; Penny, G.; Pimentel, R.; Post, D. A.; Prieto, C.; Razavi, S.; Salazar-Galán, S.; Namboothiri, A. S.; Santos, P. P.; Savenije, H.; Shanono, N. J.; Sharma, A.; Sivapalan, M.; Smagulov, Z.; Szolgay, J.; Teng, J.; Teuling, A. J.; Teutschbein, C.; Tyralis, H.; van Griensven, A.; van Schalkwyk, A. J.; van Tiel, M.; Viglione, A.; Volpi, E.; Wagener, T.; Wang-Erlandsson, L.; Wens, M.; Xia, J. 2024. The IAHS science for solutions decade, with Hydrology Engaging Local People IN a Global world (HELPING). Hydrological Sciences Journal, 50p. (Online first) [doi: https://doi.org/10.1080/02626667.2024.2355202]
Hydrology ; Water scarcity ; Transdisciplinary research ; Local knowledge ; Water security ; Prediction ; Anthropocene ; Stakeholders ; Sustainable Development Goals
(Location: IWMI HQ Call no: e-copy only Record No: H052865)
https://www.tandfonline.com/doi/epdf/10.1080/02626667.2024.2355202?needAccess=true
https://vlibrary.iwmi.org/pdf/H052865.pdf
https://vlibrary.iwmi.org/pdf/H052865.pdf
(4.65 MB) (4.65 MB)
The new scientific decade (2023-2032) of the International Association of Hydrological Sciences (IAHS) aims at searching for sustainable solutions to undesired water conditions - may it be too little, too much or too polluted. Many of the current issues originate from global change, while solutions to problems must embrace local understanding and context. The decade will explore the current water crises by searching for actionable knowledge within three themes: global and local interactions, sustainable solutions and innovative cross-cutting methods. We capitalise on previous IAHS Scientific Decades shaping a trilogy; from Hydrological Predictions (PUB) to Change and Interdisciplinarity (Panta Rhei) to Solutions (HELPING). The vision is to solve fundamental water-related environmental and societal problems by engaging with other disciplines and local stakeholders. The decade endorses mutual learning and co-creation to progress towards UN sustainable development goals. Hence, HELPING is a vehicle for putting science in action, driven by scientists working on local hydrology in coordination with local, regional, and global processes.
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