Application of Indigenous Knowledge to Flood Prevention and Management
Bibliographic record
Abstract
In the last three decades, flooding has become a nightmare associated with rainfall in all the continents of the world, as it records heavy casualties everywhere and each time it occurred. Flooding is now a big and seemingly unstoppable environmental threat to rural and urban settlements, in both developed and developing countries, regardless of their topographic traits (mountainous or lowland) and locations (coastal or landlocked). It is no longer limited to coastal communities, such as Vancouver, Bangkok and Manila or Lagos, Port-Harcourt, Warri, Sapele, Calabar, and Yenagoa in Nigeria, as many residents of landlocked cities, towns, and villages have been killed and properties destroyed by flash flood. Flooding has significantly impacted peoples’ housing, transportation, electricity, water and sanitation infrastructure, food and livelihood security. Engineering measures to address the effects of flooding through the provision of hydraulic structures seem inadequate. Indigenous knowledge (IK) has been practiced in rural communities over time to address disasters and it has been found to be effective in the protection of the lives and properties of the people and communities. This paper examines the application of indigenous knowledge to flood control and management in urban and rural communities in different parts of the world. It reviews the traditional rain prediction and flood control mechanisms as well as the coping and adaptation strategies practiced in the communities as reflected in their IK. The paper argues that it is imperative to augment western flood control practices with indigenous lood prediction and management skills to achieve sustainable flood prevention and control. Key words: Indigenous knowledge, flooding, communities, flood management, livelihood security.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".