A COMPLEMENTARY APPROACH TO PREDICTING THE MAGNITUDE OF FLOOD ALONG FOMA RIVER USING CROSS–SECTIONAL VARIABLES

Flood hazards have been on the increase in recent years, especially along the river bank. The hazards tend to impact human lives and result in severe economic damage across the world. However, forecasting the magnitude of flood especially in Nigeria across the coastal areas have been hindered by several complications, including inaccurate data, poor assessment of drainage basin, pollution, and encroachment. This study made use of the Geographical Information System (GIS) tools to derive cross-sectional variables that were significant in complementing the prediction of the magnitude of flood along Foma-river areas. Global Position System (GPS) was used to obtain the coordinate points along the river areas and Google earth imagery and topographical data of the study areas were obtained. The basin areas, streamlines, lengths of the river, and tributaries were also generated. The buffering of the river in 15 and 30 meters exposes the vulnerability status of structures along the river. Out of the 530 structures captured, 49 structures were highly vulnerable, while 105 structures were fairly vulnerable to flood hazards. The predictive accuracy of the ordered logit model approximated 81%. While a 10% error in classification was resulting from the harmonization of the precision value (0.8026) and the recall value (0.6386). The cross-sectional variables that were found to be significant at a = 0.005% are the river watersheds, the vulnerability status classification of structures across the river areas, the vulnerable structures identified, inadequate bridges and culverts along the river areas, inappropriate size of bridges and culverts, and extreme pollution along the river areas. This study is recommending the use of significant cross-sectional variables to complement the prediction of the magnitude of flood along the river banks.