The Catholic University of Malawi is a fast-growing institution of higher learning accredited by the National Council for Higher Education (NCHE) to offer Degrees, Diplomas, and Certificates. It was established by the Episcopal Conference of Malawi on October 16, 2004, and officially opened its doors in 2006. The university has seven faculties, namely Education, Law, Theology, Social Sciences, Science, Commerce and, Nursing and Midwifery.The Catholic Church has been involved in education in Malawi for over a hundred years. Its first school was established on 2 February 1902 at Nzama in Ntcheu District by three Montfort Missionaries: Fr. Pierre Bourget SMM (Superior), Fr. Augustine Prezeau SMM (who later became the first Apostolic Prefect of Shire) and Fr. Anton Winnen SMM, in charge of the first Catholic primary school in the country which he used to call ‘The University of Nzama’. The ‘University’ started with eight students – men, women, and children aged between six and sixty years.In spite of so many setbacks, by the late 1950s, the Catholic Church ran 1249 of the 2884 primary schools in Nyasaland (Malawi), and of the 24 grant-aided secondary schools and teacher training colleges, 13 were run by the Catholic Church. Just as the Church's achievements at primary school level necessitated the establishment of Catholic secondary schools in the fifties, its achievements at the secondary school level called for the establishment of a Catholic University.It is against this background that on 15 September 2004, the Bishops sent the chairperson for Education to meet the then Minister of Education to, among other things, alert him about the Episcopal Conference of Malawi's intention to request the FIC Brothers to turn Montfort Teachers Training College into a Catholic University.The then Minister of Education, Hon. Yusuf Mwawa, warmly welcomed the idea. In addition, he asked the then Principal Secretary for Education (Dr S.A. Hau) to arrange that one or two officers be included in the taskforce of the Catholic University to assist in the establishment of the university. This was indeed done and a number of task force meetings were attended by a representative of the Principal Secretary. In partnership with the Malawi Government, through the Ministry of Education, on 28 October 2006, the State President, late professor Bingu wa Mutharika officially opened The Catholic University of Malawi.Since then, student enrolment has steadily increased from 129 to 4000 plus students by 2020. The university was accredited in January 2009. In seven years of its existence, CUNIMA has officiated nineteen graduations. The university is into an affiliation agreement with the Inter Congregational Institute (ICI), and affiliation with other institutions of higher learning namely, Kachebere and St Peter's Major Seminaries..
This study aimed to analyze the quality of water and to predict water quality index and water quality class by using AI algorithm (ANN). To overcome the task, the Artificial Neural Network was used to model and predict four water quality metrics, including pollution level. These variables are, in order of importance, pH, temperature, DO, electrical conductivity (EC), Fluoride (F) and total solids (TS). Data are analyzed step by step, first to collect data to ensure that data is consistent with time intervals, clean data by removing and handling unnecessary and missing values and to normalize and standardize the data. The traditional method of estimating water quality involves costly and time-consuming statistical and laboratory analyses. Due to the concerning effects of low water quality, a speedier and less expensive alternative approach is required. For this reason, this study investigates a number of supervised machine learning algorithms to estimate the water quality class (WQC), a unique class defined based on the WQI, and the water quality index (WQI), a singular index to describe the overall quality of water. Four input factors are used in the suggested methodology: pH, Temp, DO, EC, Fluoride, and TS. The pH values, which range from 7 to 8.4, were found to be within the standards for water quality. However, the parametric values for temperature (°C), DO (mg/l), EC (dS/m), Fluoride and TS (mg/l) (mg/l) ranged from 18 to 88, 5.5 to 8.5, 80 to 840, 0.01 to 2.3 and 680 to 8580, respectively. Overall, the artificial neural network did a pretty good job of modelling and predicting the real water quality data set. The training model performance evaluation shows that the R2 values for pH, Temp, DO, EC, Fluoride, and TS are 0.7467, 0.6682, 0.7395, 0.4425, 0.3246, and 0.5525. The results of the testing model performance indicate that the R2 values for pH, Temp, DO, EC, Fluoride, and TS are 0.9984, 0.943, 0.7954, 0.3817, and 0.8313, respectively, while the results of the forecast performance evaluation indicate that the R2 values for these same parameters are 0.0837, 0.953, 0.983, 0.41, 0.702, and 0.64. It was observed that the Root Mean Squared Error (RMSE) values for pH, temperature, DO, EC, Fluoride, and TS were 0.055, 0.099, 0.05, 0.049, 0.077, and 0.068. The study findings will help researchers working on the water quality index. Some assessment indicators were calculated to evaluate the regression models’ effectiveness: The coefficient of determination (R2), mean absolute error (MAE), Relative absolute error (RAE), mean square error (MSE), Mean Percentage Error (MPE), Mean Absolute Percentage Error (MAPE), Relative root Mean Square Error (RRMSE), Relative Squared Error (RSE), Mean Square Percentage Error (MSPE) and Mean Absolute Error (MAE). For a variety of reasons, determining water suitability requires predicting Water Quality Index (WQI) and Water Quality Classification (WQC) using machine learning models for Water quality monitoring at the right instant as compared to traditional laboratory analysis, predictive models provide real-time or nearly real-time estimation of WQI and WQC, which is more efficient and economical. To determine how effective the created model was, performance evaluation of the machine learning models. Evaluation parameters based on confusion matrices were taken into consideration in order to assess the performance of the discussed models. We employed precision and accuracy in the classification of water quality. This study utilized Orange—machine learning technique and four prediction models, including linear regression, tree, KNN, and SVM and suggested an intelligent real-time water quality monitoring strategy and concentrated on quantifying and classifying water quality using machine learning techniques. The obtained WQI value was 50.83. This WQI score indicates that the water quality class (WQC) is lies in medium category, according to the acquired WQI score. The Water Quality Index (WQI) score and Water Quality Class (WQC) were quite good. The algorithm technique used for calculating water quality index was very helpful in determining the concentrations of all parameters, even if we have missing values.
The study was conducted at Bontanga irrigation scheme in Northern Region of Ghana to know the extent of water losses in the scheme, identify deficiencies leading to water losses, propose solutions for reduction of water losses, and project the impact of water losses on water demand using Water Evaluation and Planning (WEAP) model. Assessment of water losses was based on conveyance, distribution, in-field, and total water losses. Out of the seasonal irrigation water supply of 8,391,118.37 m3, total water losses of 5,766,524.23 m3 (conveyance losses: 1,208,321.04 m3, distribution losses: 2,657,635.02 m3, and in-field losses: 1,900,568.17 m3) were recorded, representing 68.70
Malawi, a landlocked country characterized by its mountainous terrain crisscrossed by the Great Rift Valley and Lake Malawi, is facing an increasing threat of landslides primarily triggered by heavy rainfall from tropical cyclones and depressions. While there are general landslide susceptibility maps available on a global and continental scale, Malawi lacks its own specific landslide hazard maps that take into account regional nuances, different types of landslides, and their triggering factors. However, factoring in these parameters is essential for accurately quantifying hazards. This contribution aims to fill this gap by proposing hazard maps that consider both spatial and temporal probabilities of landslide events. The methodology employed here is based on quantifying the probability of failure at both spatial and temporal levels, following the guidelines set forth by the Joint Technical Committee – 1 for slopes and landslides (JTC-1). To enhance the accuracy of the landslide inventory, a combination of literature review, visual remote sensing, and field surveys was used. This comprehensive data collection approach included information on the types of landslides, their activity levels, and the periods during which they are most likely to be triggered. Subsequently, susceptibility analyses were conducted for various types of landslides using a data-driven approach. Temporal analyses were carried out, taking into consideration two key factors: (i) the recurrence time of different phenomena, such as debris-flows, debris-slides, and slides from 1946 to 2019, and (ii) the rainfall patterns induced by various tropical meteorological events, as defined by the World Meteorological Organization and Meteo-France. the computation of exceedance probabilities based on the Poisson distribution, predicting the likelihood of landslide reactivation for six different return periods, ranging from 1 to 100 years, following various typical meteorological events. Subsequently, susceptibility analyses were conducted for various types of landslides using a data-driven approach. Temporal analyses were carried out, taking into consideration two key factors: (i) the recurrence time of different phenomena, such as debris-flows, debris-slides, and slides from 1946 to 2019, and (ii) the rainfall patterns induced by various tropical meteorological events, as defined by the World Meteorological Organization and Meteo-France. the computation of exceedance probabilities based on the Poisson distribution, predicting the likelihood of landslide reactivation for six different return periods, ranging from 1 to 100 years, following various typical meteorological events. Ultimately, this methodology facilitates the development of various spatio-temporal landslide risk scenarios on a national scale.
Beds and bank enclosures are practices that are understudied within water resources grabbing. The practice involves property owners, such as dwelling houses, resorts, lodges, hotels, farms, and others enclosing their property bordering watercourses like rivers and lakes, thereby excluding local communities’ access to the said common resources in their quest for livelihood activities. This is done despite the Malawian National Water Resources Authority’s 2013 Act, which prohibits cultivation or carrying out any activity within the beds and banks of watercourses and lakes and their adjacent land strips, except as determined by the Authority. Water grabbing is usually considered on a larger scale, involving large-scale transactions for the production of export crops and corporate agendas. Less often, it involves small-scale watercourse enclosures that are done locally. Using Political Ecology Framework, the paper argues that bed and bank enclosures are new form of water resources grabbing. The practice perpetuates poverty as communities are denied access to one of the most important assets in their livelihoods. The paper contradicts with the popular Malthusian perspective in Political Ecology Framework that poor people are a cause of water resources degradation, but rather the narrative is utilized by local capitalists to marginalize communities from accessing water related resources.
Flood vulnerability assessment (FVA) informs the disaster risk reduction and preparedness process in both rural and urban areas. However, many flood-vulnerable regions like Malawi still lack FVA supporting frameworks in all phases (pre-trans-post disaster). Partly, this is attributed to lack of the evidence-based studies to inform the processes. This study was therefore aimed at assessing households’ flood vulnerability (HFV) in rural and urban informal areas of Malawi, using case studies of Traditional Authority (T/A) Kilupula of Karonga District (KD) and Mtandire Ward in Lilongwe City (LC). A household survey was used to collect data from a sample of 545 household participants. Vulnerability was explored through a combination of underlying vulnerability factors (UVFs)-physical-social-economic-environmental and cultural with vulnerability components (VCs)-exposure-susceptibility and resilience. The UVFs and VCs were agglomerated using binomial multiple logit regression model. Variance inflation factor (VIF) was used to check the multicollinearity of variables in the regression model. HFV was determined based on the flood vulnerability index (FVI). The data were analysed using Multiple Correspondence Analysis (MCA), artificial neural network (ANN) and STATA. The results reveal a total average score of high vulnerability (0.62) and moderate vulnerability (0.52) on MCA in T/A Kilupula of Karonga District and Mtandire Ward of Lilongwe City respectively. The FVI revealed very high vulnerability on enviroexposure factors (EEFs) ( 0.9 ) in LC and (0.8 ) in KD, followed by ecoresilience factors (ERFs) (0.8) in KD and (0.6 ) in LC and physioexposure factors (PEFs) ( 0.5) in LC besides 0.6 in KD for the combined UVFs and VCs. The study concludes that the determinants of households’ flood vulnerability are place settlement, low-risk knowledge, communication accessibility, lack of early warning systems, and limited access to income of household heads. The study recommends that an FVA framework should be applied to strengthen the political, legal, social, and economic responsibilities of government for building the resilience of communities and supporting planning and decision-making processes in flood risk management.
