Kabul River Flow Prediction Using Automated ARIMA Forecasting: A Machine Learning Approach

The water level in a river defines the nature of flow and is fundamental to flood analysis. Extreme fluctuation levels rivers, such as floods droughts, are catastrophic every manner; therefore, forecasting at an early stage would prevent possible disasters relief efforts could be set up on time. This study aims digitally model Kabul River alleviate effects any change this downstream. used machine learning tool known automatic autoregressive integrated moving average for statistical methodological analysis flow. Based hydrological data collected from Swat, 2011–2030 were forecasted, which based lowest value Akaike Information Criterion 9.216. It was concluded that started increase year 2011 till it reached its peak 2019–2020, then will maintain maximum 250 cumecs minimum 10 2030. need research justified prove helpful establishing guidelines designers, planning management water, hydropower engineering projects, indicator weather prediction, people who greatly dependent their survival.