CHALLENGES AND SOLUTIONS OF SEWERAGE NETWORKS IN KARBALA, IRAQ FOR FLOOD MONITORING USING SMART SENSORS AND GIS

Floods in urban areas pose major challenges to infrastructure, especially in third world countries. The sewage network in Karbala, Iraq, faces recurring difficulties and problems from floods, especially in the rainy season, taking into account the large population density in Karbala. The design of the sewage network does not meet the need for this. This study came to build a model based on one of the artificial intelligence techniques, which is machine learning through Support Vector Machines (SVM) to analyze data coming from smart sensors and Geographic Information Systems (GIS). The classification that links variable weights is used according to the data coming from the sensors to enhance the work of the classifier and obtain a classification accuracy of up to 89.7% in real time. Enhancing smart decision-making is the most important stage in improving and building sewage networks, which are the basis of urban cities and their planning. In the future, the deep learning mechanism can be combined with machine learning to reach more accurate predictions and faster data analysis.