Algorithm 1 Water Distribution System Optimization

1:Globals: $threshold\_optimization=0.8$, $unit\_cost=0.5$, $total\_resources=0$  2:Load Data: $X\leftarrow df\left[{[}^{\prime }base\_deman{d}^{\prime }]\right]$, $y\leftarrow df\left[{[}^{\prime }lps\_deman{d}^{\prime }]\right]$  3:Train Model and Evaluate:  4:$X\_train,X\_test,y\_train,y\_test\leftarrow$ train_test_split(X, y, 0.2, 42)  5:$model\leftarrow$ LinearRegression().fit(X_train, y_train)  6:$mae\leftarrow$ mean_absolute_error(y_test, $model.predict(X\_test)$)  7:Optimization Process:  8:$df\leftarrow$ pd.read_excel(’city_$L_{D}and{B}_D$.xlsx’, names=[’node_id’, ’base_demand’, ’lps_demand’])  9:for $index,row$ in  $df.iterrows\left(\right)$  do 10: $node\_id\leftarrow row{[}^{\prime }node\_i{d}^{\prime }]$, $base\_demand,lps\_demand\leftarrow row\left[{[}^{\prime }base\_deman{d}^{\prime }{,}^{\prime }lps\_deman{d}^{\prime }]\right]$ 11: $resources\leftarrow base\_demand\times unit\_cost$ 12: if $lps\_demand<base\_demand\times threshold\_optimization$ then 13:  print(“Successful optimization at node”, $node\_id$) 14:  $resources\leftarrow resources\times 0.9$ 15: else 16:  print(“Optimization was not achieved in the node”, $node\_id$) 17: end if 18: $total\_resources\leftarrow total\_resources+resources$ 19:end for