Doctoral Degrees (Logistics)

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Browsing Doctoral Degrees (Logistics) by Author "Fouotsa Manfouo, Noe Careme"

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    Application of long short-term memory artificial neural networks to forecast water supply and demand in the Lake Chad Basin
    (Stellenbosch : Stellenbosch University, 2020-12) Fouotsa Manfouo, Noe Careme; Potgieter, Linke; Nel, Johanna H.; Stellenbosch University. Faculty of Economic and Management Sciences. Dept. of Logistics. Logistics.
    ENGLISH ABSTRACT: The implementation of effective water resources management in developing countries in general and in the Lake Chad Basin in particular, is hindered by the absence of reliable information on both the net water supply, as well as on the agricultural water demand. The main purpose of this research is to provide a methodology to determine and forecast total water supply and water demand in the context of scarce data for water resources management. In order to develop a forecasting methodology, a literature survey is first performed to understand the current environment and methodology of water resources management in the Lake Chad Basin, to highlight the main problems faced within the context, and to identify the opportunity for applied research. As part of this investigation, different stakeholders were visited during a field trip to the Lake Chad Basin. The main water users identified in the Lake Chad Basin do not have historical data on agricultural water demands, making it difficult to understand current water demand requirements or estimate future demand in the Lake Chad Basin. Literature available on the Lake Chad Basin were also considered. A hydrological model was developed in 2011 by Bader, Lemoalle, and Leblanc and reported on in the paper Modèle hydrologique du Lac Tchad [16]. The model provides information on the lake storage for the period 1956 to 2011, however, it does not consider upstream diversion. Therefore, the output of the model does not allow an exhaustive estimation of water supply in the Lake Chad Basin. In addition, the model is data intensive and uses variables that are neither easy to obtain, nor straightforward to compute, and requires expert hydrological knowledge to extend the use of the model for future water supply estimation beyond 2011. Moreover, there are currently no model developed for estimating water demand in the Lake Chad Basin. Long short-term memory is an artificial recurrent neural network that have been shown to perform exceptionally well in the context of time series forecasting, due to its ability to incorporate lags of unknown duration in the network structure. Despite the good track record of this methodology in forecasting time series, it is not widely used in the literature for water supply and demand estimation. In this dissertation, multivariate time series forecasting with long short-term memory is investigated as an alternative methodology for different aspects of water supply and demand estimation. Pearson correlation, random forest, extra trees classifiers and principal component analysis are investigated as input selection approaches to increase prediction accuracy. For water supply estimation, a lake storage forecasting model as well as a streamflow forecasting model are developed. Results indicate that long short-term memory can be used to predict Lake Chad Basin storage, with better performances than the state of the art results, obtained from artificial neural networks and support vector regression. The multivariate approach indicates that atmospheric data are both good and easily obtainable data for lake storage forecasting. The input variables, selected with both the principal component analysis and random forest approach are recommended for streamflow forecasting in the Lake Chad Basin. Random forest occupies the second position, by producing better predictions in the Ndjamena gauging station. A long-term temperature forecasting model as well as a precipitation forecasting model were developed and the outputs were used as input in the CROPWAT software to determine the irrigation water requirement per hectare per crop type. A comparison between the widely used statistical downscaling model and the forecasting models for long-term temperatures and precipitations developed in this research indicate better accuracy using the multivariate long short-term memory approach. Both the root mean square error and the mean absolute percentage error used to check the performances of the models indicate commendable accuracy. Four population dynamics models, namely the malthusian growth model; the logistic growth models with both constant and dynamic rates, as well a logistic growth model with dynamic rate and species interaction, are developed to estimate the size of land used for both crop and livestock, and to finally predict the total agricultural water demand in the Lake Chad Basin. The models are parameterised using long short-term memory. A case by case investigation of prediction performances across the three countries indicates that the malthusian growth approach produces better performances in 9 cases, the logistic growth model with constant rate performs better in 4 cases, and the logistic growth model with dynamic rate performs better in 7 cases. The malthusian approach is more suitable for variables with unstable trends, the logistic model with constant rate is more suitable for variables with almost concave or convex shapes and the logistic growth with dynamic rate is the most useful long-term crop land-use and livestock population forecasting. Finally, the best performing models for crop land-use and livestock population are downscaled to main water users level, in order to estimate total water demand per crop type and per livestock type. The investigation of the four population dynamics models, on both the crop land-use and livestock population dynamics, the characterisation of the competition type between species in the Lake Chad Basin case study as well as the estimation of water demand at water users’ level is a new contribution to literature.