Your search found 11 records
1 Wooldridge, J. M. 2010. Econometric analysis of cross section and panel data. 2nd ed. Cambridge, MA, USA: MIT Press. 1064p.
Statistical methods ; Econometrics ; Mathematical models ; Linear models ; Cross sectional analysis ; Simultaneous equation analysis ; Single equation analysis ; Regression analysis ; Multivariate analysis ; Non linear programming ; Estimation ; Data analysis ; Cluster sampling ; Testing
(Location: IWMI HQ Call no: 330.015195 G000 WOO Record No: H047137)
(0.88 MB)
2 Wooldridge, J. M. 2010. Econometric analysis of cross section and panel data. 2nd ed. Cambridge, MA, USA: MIT Press. 1064p.
Statistical methods ; Econometrics ; Mathematical models ; Linear models ; Cross sectional analysis ; Simultaneous equation analysis ; Single equation analysis ; Regression analysis ; Multivariate analysis ; Non linear programming ; Estimation ; Data analysis ; Cluster sampling ; Testing
(Location: IWMI HQ Call no: 330.015195 G000 WOO c2 Record No: H047138)
(0.88 MB)
3 Cameron, A. C.; Trivedi, P. K. 2010. Microeconometrics using stata. Rev. ed. College Station, TX, USA: Stata Press. 706p.
Microeconomics ; Statistical methods ; Econometrics ; Mathematical models ; Computer software ; Data management ; Computer programming ; Optimization methods ; Linear models ; Regression analysis ; Non linear programming ; Computer graphics ; Simulation ; Testing
(Location: IWMI HQ Call no: 330.015195 G000 CAM Record No: H047139)
(1.57 MB)
4 Cameron, A. C.; Trivedi, P. K. 2010. Microeconometrics using stata. Rev. ed. College Station, TX, USA: Stata Press. 706p.
Microeconomics ; Statistical methods ; Econometrics ; Mathematical models ; Computer software ; Data management ; Computer programming ; Optimization methods ; Linear models ; Regression analysis ; Non linear programming ; Computer graphics ; Simulation ; Testing
(Location: IWMI HQ Call no: 330.015195 G000 CAM c2 Record No: H047140)
(1.57 MB)
5 Anwar, Arif A.; Bhatti, Muhammad Tousif; de Vries, T. T. 2016. Canal operations planner. I: maximizing delivery performance ratio. Journal of Irrigation and Drainage Engineering, 142(12):1-12. [doi: https://doi.org/10.1061/(ASCE)IR.1943-4774.0001091]
Irrigation systems ; Irrigation canals ; Irrigation operation ; Seasonal cropping ; Performance evaluation ; Equity ; Mathematical models ; Linear models ; Linear programming / Pakistan / Punjab / Indus Basin Irrigation System
(Location: IWMI HQ Call no: e-copy only Record No: H047652)
(0.87 MB)
A key operational objective for the management of the Indus Basin Irrigation System of Pakistan is the distribution of water among tertiary canals in a transparent and equitable manner. Decisions on canal operations are disseminated as a Canal Operation Plan, or a Rotational Program, for each crop season for every canal system. The current practice for developing these plans is qualitative based on heuristics that have remain unchanged since the early development of this vast irrigation system. This paper uses operations research tools to develop a Canal Operations Planner. Allocation cost is defined as a function of the delivery performance ratio and maximizing this function. The performance of the modules is evaluated using spillage and the Gini index as a measure of equity. Two models, namely; linear programme-delivery performance ratio (LP-DPR) and non linear programme-delivery performance ratio (NLP-DPR) are presented and the results are compared to performance under current canal planning and operational practice. Both models improve the equity when compared to existing operations. The NLP-DPR model outperforms the LP-DPR both on equity and minimizing spillage.
6 Tourian, M. J.; Schwatke, C.; Sneeuw, N. 2017. River discharge estimation at daily resolution from satellite altimetry over an entire river basin. Journal of Hydrology, 546:230-247. [doi: https://doi.org/10.1016/j.jhydrol.2017.01.009]
River basins ; Flow discharge ; Water levels ; Satellite observation ; Tributaries ; Hydrology ; Linear models ; Time series analysis ; Estimation ; Performance indexes ; Uncertainty ; Deltas / Mali / Guinea / Nigeria / Benin / Niger River Basin / Niger Delta / Benue River / Bani River
(Location: IWMI HQ Call no: e-copy only Record No: H048040)
(5.12 MB)
One of the main challenges of hydrological modeling is the poor spatiotemporal coverage of in situ discharge databases which have steadily been declining over the past few decades. It has been demonstrated that water heights over rivers from satellite altimetry can sensibly be used to deal with the growing lack of in situ discharge data. However, the altimetric discharge is often estimated from a single virtual station suffering from coarse temporal resolution, sometimes with data outages, poor modeling and inconsistent sampling. In this study, we propose a method to estimate daily river discharge using altimetric time series of an entire river basin including its tributaries. Here, we implement a linear dynamic model to (1) provide a scheme for data assimilation of multiple altimetric discharge along a river; (2) estimate daily discharge; (3) deal with data outages, and (4) smooth the estimated discharge. The model consists of a stochastic process model that benefits from the cyclostationary behavior of discharge. Our process model comprises the covariance and cross-covariance information of river discharge at different gauges. Combined with altimetric discharge time series, we solve the linear dynamic system using the Kalman filter and smoother providing unbiased discharge with minimum variance. We evaluate our method over the Niger basin, where we generate altimetric discharge using water level time series derived from missions ENVISAT, SARAL/AltiKa, and Jason-2. Validation against in situ discharge shows that our method provides daily river discharge with an average correlation of 0.95, relative RMS error of 12%, relative bias of 10% and NSE coefficient of 0.7. Using a modified NSE-metric, that assesses the non-cyclostationary behavior, we show that our estimated discharge outperforms available legacy mean daily discharge.
7 Zereyesus, Y. A.; Embaye, W. T.; Tsiboe, F.; Amanor-Boadu, V. 2017. Implications of non-farm work to vulnerability to food poverty-recent evidence from northern Ghana. World Development, 91:113-124. [doi: https://doi.org/10.1016/j.worlddev.2016.10.015]
Food security ; Nonfarm income ; Food consumption ; Household expenditure ; Forecasting ; Non-farm employment ; Participation ; Food insecurity ; Poverty ; Hunger ; Public health ; Socioeconomic environment ; Linear models ; Regression analysis / Ghana / Brong Ahafo Region / Northern Region / Upper East Region / Upper West Region
(Location: IWMI HQ Call no: e-copy only Record No: H048046)
http://www.sciencedirect.com/science/article/pii/S0305750X16305174/pdfft?md5=da180e20bb4e04280feb14bdeb445e03&pid=1-s2.0-S0305750X16305174-main.pdf
https://vlibrary.iwmi.org/pdf/H048046.pdf
https://vlibrary.iwmi.org/pdf/H048046.pdf
(0.33 MB) (340 KB)
Using survey data from northern Ghana, this study seeks to establish the impact of participation in non-farm work on the vulnerability of resource poor households to food poverty. Vulnerability to food poverty is assessed based on expected future food expenditure of households. The potential endogeneity problem associated with participation in non-farm work by households is overcome using a novel instrumental variable approach. Analysis of the determinants of expected future food expenditure is done using a standard Feasible Generalized Least Squares (FGLS) method. Demographic and socioeconomic variables, location variables, and household facilities are included in the model as control variables. Our study finds that participation in non-farm work significantly increased the future expected food consumption, thereby alleviating the vulnerability of households to food poverty. Our study also confirms that current food poverty and future food poverty, i.e., vulnerability to food poverty, are not independent from each other. Non-farm work plays a crucial role in providing the means to overcome the risk of food poverty in these resource poor households. Policies that promote off-farm income generating activities, such as small businesses and self-employment, as well as the creation and support of businesses that absorb extra labor from the farm, should be encouraged in the study region. Because households in the study region are exposed to above average levels of hunger and food poverty, the study recommends the government of Ghana and development partners to take measures that enhance the resilience of these resource poor households.
8 Louviere, J. J.; Hensher, D. A.; Swait, J. D.; Adamowicz, W. 2000. Stated choice methods: analysis and applications. Cambridge, UK: Cambridge University Press. 402p.
Consumer behaviour ; Decision making ; Mathematical models ; Linear models ; Experimental design ; Project design ; Strategies ; Marketing techniques ; Transport ; Environmental modelling ; Case studies ; Statistical methods ; Estimation ; Valuation ; Performance testing
(Location: IWMI HQ Call no: 658.8342 G000 LOU Record No: H048586)
(0.45 MB)
9 Akpoti, K.; Kabo-bah, A. T.; Dossou-Yovo, E. R.; Groen, T. A.; Zwart, Sander J. 2020. Mapping suitability for rice production in inland valley landscapes in Benin and Togo using environmental niche modeling. Science of the Total Environment, 709:136165. [doi: https://doi.org/10.1016/j.scitotenv.2019.136165]
Land suitability ; Rice ; Agricultural production ; Environmental modelling ; Linear models ; Forecasting ; Uncertainty ; Water productivity ; Soil water content ; Rainfed farming ; Climatic data ; Soil chemicophysical properties ; Socioeconomic environment ; Valleys / Benin / Togo
(Location: IWMI HQ Call no: e-copy only Record No: H049495)
(5.47 MB)
Inland valleys (IVs) in Africa are important landscapes for rice cultivation and are targeted by national governments to attain self-sufficiency. Yet, there is limited information on the spatial distribution of IVs suitability at the national scale. In the present study, we developed an ensemble model approach to characterize the IVs suitability for rainfed lowland rice using 4 machine learning algorithms based on environmental niche modeling (ENM) with presence-only data and background sample, namely Boosted Regression Tree (BRT), Generalized Linear Model (GLM), Maximum Entropy (MAXNT) and Random Forest (RF). We used a set of predictors that were grouped under climatic variables, agricultural water productivity and soil water content, soil chemical properties, soil physical properties, vegetation cover, and socio-economic variables. The Area Under the Curves (AUC) evaluation metrics for both training and testing were respectively 0.999 and 0.873 for BRT, 0.866 and 0.816 for GLM, 0.948 and 0.861 for MAXENT and 0.911 and 0.878 for RF. Results showed that proximity of inland valleys to roads and urban centers, elevation, soil water holding capacity, bulk density, vegetation index, gross biomass water productivity, precipitation of the wettest quarter, isothermality, annual precipitation, and total phosphorus among others were major predictors of IVs suitability for rainfed lowland rice. Suitable IVs areas were estimated at 155,000–225,000 Ha in Togo and 351,000–406,000 Ha in Benin. We estimated that 53.8% of the suitable IVs area is needed in Togo to attain self-sufficiency in rice while 60.1% of the suitable IVs area is needed in Benin to attain self-sufficiency in rice. These results demonstrated the effectiveness of an ensemble environmental niche modeling approach that combines the strengths of several models.
10 Huang, Q.; Yin, D.; He, C.; Yan, J.; Liu, Z.; Meng. S.; Ren, Q.; Zhao, R.; Inostroza, L. 2020. Linking ecosystem services and subjective well-being in rapidly urbanizing watersheds: insights from a multilevel linear model. Ecosystem Services, 43:101106. (Online first) [doi: https://doi.org/10.1016/j.ecoser.2020.101106]
Ecosystem services ; Assessment ; Watersheds ; Socioeconomic environment ; Urbanization ; Rural communities ; Sustainability ; Regional planning ; Hygroscopicity ; Carbon sequestration ; Ecological factors ; Linear models / China / Hebei / Baiyangdian Watershed
(Location: IWMI HQ Call no: e-copy only Record No: H049673)
(0.84 MB)
In rapidly urbanizing watersheds with conflicts between socioeconomic development and ecological protection, understanding the relationship between ecosystem services (ESs) and human well-being is important for regional sustainability. However, quantifying their relationship over multiple scales remains challenging. We selected a typical rapidly urbanizing watershed, the Baiyangdian watershed in China, and used surveys and a multilevel linear model to analyze the influence of regional ESs and individual characteristics on subjective well-being (SWB). Our results showed that the multilevel linear model could effectively capture the influences of regional ESs on the residents’ SWB. For the watershed, 95.9% of the total variance in the residents’ SWB was attributed to variation between individuals, and the remaining 4.1% was attributed to variation between regions. The SWB of rural residents was more likely to be affected by regional ESs than urban residents. In the Baiyangdian watershed, which has a water supply shortage, the SWB of low-income and elderly residents in the rural areas was more sensitive to water retention services, and the association was significant. The results suggest that in rapidly urbanizing watersheds, government should pay attention to maintaining and improving key regulating services to effectively maintain and promote the SWB of rural residents and regional sustainability.
11 Torres, A. B. B.; da Rocha, A. R.; Coelho da Silva, T. L.; de Souza, J. N.; Gondim, R. S. 2020. Multilevel data fusion for the internet of things in smart agriculture. Computers and Electronics in Agriculture, 171:105309. [doi: https://doi.org/10.1016/j.compag.2020.105309]
Decision support systems ; Internet ; Agriculture ; Irrigation ; Soil moisture ; Evapotranspiration ; Energy consumption ; Linear models ; Sensors ; Crops ; Cashews ; Coconuts / Brazil / Paraipaba
(Location: IWMI HQ Call no: e-copy only Record No: H049724)
(7.91 MB)
The Internet of Things (IoT) aims to enable objects to sense, identify, and analyze the world, but to achieve such goal cost-effectively, it should involve low-cost solutions. That implies a series of limitations, such as small battery life, limited storage capabilities, low accuracy, and imprecise sensors. Data fusion is one of the most widely used methods for improving sensor accuracy and providing a more precise decision. Therefore, we propose Hydra, a multilevel data fusion architecture, to improve sensor accuracy, identify application target events, and make more accurate decisions. Hydra is composed of three layers: low-level (sensor data fusion), medium-level (events and decision making), and high-level (decision fusion based on multiple applications). In partnership with Embrapa (Brazilian Agricultural Research Corporation), we instantiated Hydra for the smart agriculture domain, and we also developed two applications aiming smart water management. The first application goal was to determine the need for irrigation based on soil moisture levels, and the second ascertained the adequate irrigation time by estimating the crop’s evapotranspiration (rate of water evaporation by the soil and transpiration by plants). We performed a set of experiments to assess Hydra: (i) evaluation of methods to detect and remove outliers; (ii) analyze data resulting from the applications; (iii) the use of machine learning to create a new accurate evapotranspiration model based on the sensors data. The results indicate that a combination of the ESD method (Extreme Studentized Deviate) and WRKF filter (Weighted Outlier-Robust Kalman Filter) was the best method to identify and remove outliers. Moreover, we generated an evapotranspiration model using the SVM (Support Machine Vector) quadratic machine-learning model that produced values close to the evapotranspiration reference model (Penman-Monteith).
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