Your search found 12 records
1 Vanclooster, M.; Mallants, D.; Diels, J. 1993. On the use of time domain reflectometry (TDR) for assessing soil water and salinity concentration. Siphon, No.14:13-17.
Irrigation equipment ; Irrigation scheduling ; Salinity ; Soil moisture ; Mathematical models / Germany
(Location: IWMI-HQ Call no: P 2754 Record No: H012555)
2 Espino, A.; Mallants, D.; Vanclooster, M.; Feyen, J. 1996. Cautionary notes on the use of pedotransfer functions for estimating soil hydraulic properties. Agricultural Water Management, 29(3):235-253.
(Location: IWMI-HQ Call no: PER Record No: H018213)
3 Chang, Y.; Vanclooster, M.; Hubrechts, L.; Feyen, J. 1996. Multicriteria decision analysis in irrigation scheduling. In Camp, C. R.; Sadler, E. J.; Yoder, R. E. (Eds.), Evapotranspiration and irrigation scheduling: Proceedings of the International Conference, November 3-6, 1996, San Antonio Convention Center, San Antonio, Texas. St. Joseph, MI, USA: ASAE. pp.1128-1133.
Irrigation scheduling ; Maize ; Simulation models ; Optimization ; Soil water ; Nitrogen ; Leaching ; Plant growth ; Crop yield ; Case studies / Belgium / Ophoven-Kinrooi Irrigation Project
(Location: IWMI-HQ Call no: 631.7.1 G000 CAM Record No: H020713)
4 Vanclooster, M.; Boesten, J. J. T. I.; Trevisan, M.; Brown, C. D.; Capri, E.; Eklo, O. M.; Gottesbnren, B.; Gouy, V.; van der Linden, A. M. A. 2000. A European test of pesticide-leaching models: Methodology and major recommendations. Agricultural Water Management, 44(1-3):1-19.
Pesticide residues ; Leaching ; Simulation models ; Risks ; Assessment / Netherlands / Germany / Italy / UK
(Location: IWMI-HQ Call no: PER Record No: H025964)
5 Vanclooster, M.; Boesten, J. J. T. I. 2000. Application of pesticide simulation models to the Vredepeel dataset: I. Water, solute and heat transport. Agricultural Water Management, 44(1-3):105-117.
Pesticide residues ; Leaching ; Simulation models ; Calibrations ; Hydraulics ; Sandy soils ; Soil water ; Evapotranspiration ; Soil temperature / Netherlands
(Location: IWMI-HQ Call no: PER Record No: H025970)
6 Vanclooster, M.; Ducheyne, S.; Dust, M.; Vereecken, H. 2000. Evaluation of pesticide dynamics of the WAVE-model. Agricultural Water Management, 44(1-3):371-388.
Models ; Pesticide residues ; Leaching ; Evaluation ; Simulation ; Soil moisture ; Sandy soils ; Soil water ; Drainage ; Water balance ; Plant growth ; Lysimetry / Netherlands / Germany
(Location: IWMI-HQ Call no: PER Record No: H025984)
7 Fernandez, J. E.; Slawinski, C.; Moreno, F.; Walczak, R. T.; Vanclooster, M.. 2002. Simulating the fate of water in a soil-crop system of a semi-arid Mediterranean area with the WAVE 2.1 and the EURO-ACCESS-II models. Agricultural Water Management, 56(2):113-129.
Simulation models ; Calibration ; Crop production ; Water use ; Sandy soils ; Maize ; Furrow irrigation ; Soil moisture
(Location: IWMI-HQ Call no: PER Record No: H030337)
8 Tilmant, A.; Fortemps, P.; Vanclooster, M.. 2002. Effect of averaging operators in fuzzy optimization of reservoir operation. Water Resources Management, 16(1):1-22.
Reservoir operation ; Decision making ; Decision support tools ; Models ; Stochastic processes ; Simulation ; Water storage ; Hydroelectric schemes / Brazil / Uruguay River Basin
(Location: IWMI-HQ Call no: PER Record No: H030343)
9 Persoons, E.; Vanclooster, M.; Desmed, A. 2002. Flood hazard causes and flood protection recommendations for Belgan River Basins. Water International, 27(2):202-207.
Flood control ; River basins ; Hydraulics ; Infrastructure ; Ecosystems ; Hydrology ; Land use ; Runoff ; Flood plains ; Climate / Belgium
(Location: IWMI-HQ Call no: PER Record No: H030796)
10 Ritter, A.; Hupet, F.; Munoz-Carpena, R.; Lambot, S.; Vanclooster, M.. 2003. Using inverse methods for estimating soil hydraulic properties from field data as an alternative to direct methods. Agricultural Water Management, 59(2):77-96.
(Location: IWMI-HQ Call no: PER Record No: H031525)
11 Abbasi, F.; Javaux, M.; Vanclooster, M.; Feyen, J.; Wyseure, G.; Nziguheba, G. 2006. Experimental study of water flow and sulphate transport at monolith scale. Agricultural Water Management, 79(1):93-112.
(Location: IWMI-HQ Call no: PER Record No: H038287)
12 Ouedraogo, I.; Defourny, P.; Vanclooster, M.. 2019. Application of random forest regression and comparison of its performance to multiple linear regression in modeling groundwater nitrate concentration at the African continent scale. Hydrogeology Journal, 27(3):1081-1098. (Special issue: Groundwater in Sub-Saharan Africa) [doi: https://doi.org/10.1007/s10040-018-1900-5]
Groundwater pollution ; Nitrates ; Contamination ; Fertilizer application ; Geographical information systems ; Regression analysis ; Models ; Forecasting ; Performance evaluation ; Soil types ; Land use / Africa South of Sahara
(Location: IWMI HQ Call no: e-copy only Record No: H049363)
https://link.springer.com/content/pdf/10.1007%2Fs10040-018-1900-5.pdf
https://vlibrary.iwmi.org/pdf/H049363.pdf
https://vlibrary.iwmi.org/pdf/H049363.pdf
(2.09 MB) (2.09 MB)
Groundwater management decisions require robust methods that allow accurate predictive modeling of pollutant occurrences. In this study, random forest regression (RFR) was used for modeling groundwater nitrate contamination at the African continent scale. When compared to more conventional techniques, key advantages of RFR include its nonparametric nature, its high predictive accuracy, and its capability to determine variable importance. The latter can be used to better understand the individual role and the combined effect of explanatory variables in a predictive model. In the absence of a systematic groundwater monitoring program at the African continent scale, the study used the groundwater nitrate contamination database for the continent obtained from a meta-analysis to test the modeling approach; 250 groundwater nitrate pollution studies from the African continent were compiled using the literature data. A geographic information system database of 13 spatial attributes was collected, related to land use, soil type, hydrogeology, topography, climatology, type of region, and nitrogen fertilizer application rate, and these were assigned as predictors. The RFR performance was evaluated in comparison to the multiple linear regression (MLR) methods. By using RFR, it was possible to establish which explanatory variables influence the occurrence of nitrate pollution in groundwater (population density, rainfall, recharge, etc.). Both the RFR and MLR techniques identified population density as the most important variable explaining reported nitrate contamination. However, RFR has a much higher predictive power (R2 = 0.97) than a traditional linear regression model (R2 = 0.64). RFR is therefore considered a very promising technique for large-scale modeling of groundwater nitrate pollution.
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