Elevating the accuracy of streamflow forecasting has always been a challenge. This paper proposes a three-step artificial intelligence model improvement for streamflow forecasting. Step 1 uses long short-term memory (LSTM), an improvement on the conventional artificial neural network (ANN). Step 2 performs multi-step ahead forecasting while establishing the rates of change as a new approach. Step 3 further improves the accuracy through three different kinds of optimization algorithms. The Stormwater and Road Tunnel project in Kuala Lumpur is the study area. Historical rainfall data of 14 years at 11 telemetry stations are obtained to forecast the flow at the confluence located next to the control center. Step 1 reveals that LSTM is a better model than ANN with R 0.9055, MSE 17,8532, MAE 1.4365, NSE 0.8190 and RMSE 5.3695. Step 2 unveils the rates of change model that outperforms the rest with R = 0.9545, MSE = 8.9746, MAE = 0.5434, NSE = 0.9090 and RMSE = 2.9958. Finally, Stage 3 is a further improvement with R = 0.9757, MSE = 4.7187, MAE = 0.4672, NSE = 0.9514 and RMSE = 2.1723 for the bat-LSTM hybrid algorithm. This study shows that the δQ model has consistently yielded promising results while the metaheuristic algorithms are able to yield additional improvement to the model’s results.

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Water security and urban flooding have become major sustainability issues. This paper presents a novel method to introduce rates of change as the state-of-the-art approach in artificial intelligence model development for sustainability agenda. Multi-layer perceptron (MLP) and deep learning long short-term memory (LSTM) models were considered for flood forecasting. Historical rainfall data from 2008 to 2021 at 11 telemetry stations were obtained to predict flow at the confluence between Klang River and Ampang River. The initial results of MLP yielded poor performance beneath normal expectations, which was R = 0.4465, MAE = 3.7135, NSE = 0.1994 and RMSE = 8.8556. Meanwhile, the LSTM model generated a 45% improvement in its R-value up to 0.9055. Detailed investigations found that the redundancy of data input that yielded multiple target values had distorted the model performance. Qt was introduced into input parameters to solve this issue, while Qt+0.5 was the target value. A significant improvement in the results was detected with R = 0.9359, MAE = 0.7722, NSE = 0.8756 and RMSE = 3.4911. When the rates of change were employed, an impressive improvement was seen for the plot of actual vs. forecasted flow. Findings showed that the rates of change could reduce forecast errors and were helpful as an additional layer of early flood detection.