Projected Water Levels and Identified Future Floods: A Comparative Analysis for Mahaweli River, Sri Lanka

Abstract

The Rainfall-Runoff (R-R) relationship is essential to the hydrological cycle. Sophisticated hydrological models can accurately investigate R-R relationships; however, they require many data. Therefore, machine learning and soft computing techniques have taken the attention in the environment of limited hydrological, meteorological, and geological data. The accuracy of such models depends on the various parameters, including the quality of inputs and outputs and the used algorithms. However, identifying a perfect algorithm is still challenging. This study develops a fuzzy logic-based algorithm called Cascaded-ANFIS to accurately predict runoff based on rainfall. The model was compared against three regression algorithms: Long Short-Term Memory, Grated Recurrent Unit, and Recurrent Neural Networks. These algorithms have been selected due to their outstanding performances in similar studies. The models were tested on the Mahaweli River, the longest in Sri Lanka. The results showcase that the Cascaded-ANFISbased model outperforms the other algorithms. The correlation coefficient of each algorithm's predictions was 0.9330, 0.9120, 0.9133, 0.8915, 0.6811, 0.6811, and 0.6734 for the Cascaded-ANFIS, LSTM, GRU, RNN, Linear, Ridge, and Lasso regression models respectively. Hence, this study concludes that the proposed algorithm is 21% more accurate than the second-best LSTM algorithm. In addition, Shared Socio-economic Pathways (SSP2-4.5 and SSP5-8.5 scenarios) were used to generate future rainfalls, forecast the near-future and mid-future water levels, and identify potential flood events. The future forecasting results indicate a decrease in flood events and magnitudes in both SSP2-4.5 and SSP5-8.5 scenarios. Furthermore, the SSP5-8.5 scenario shows drought weather from May to August yearly. The results of this study can effectively be used to manage and control water resources and mitigate flood damages.

Impact of economic indicators on rice production: A machine learning approach in Sri Lanka

“…RMSE is a widely used metric that measures the average magnitude of the errors between predicted ( y i ) and observed ( ) values ( Eq 2 ) [ 38 ]. It comprehensively assesses the model’s accuracy, where lower values indicate better performance.…”

Traditional Water Governance Practices for Flood Mitigation in Ancient Sri Lanka

“…It constructed new Western-style water reservoirs such as Kotmale, Victoria, and Maduru Oya. Kotmale reservoir was specifically designed for flood control by making it possible to transfer flood water from Polgolla to the dry zone where restored ancient tanks are located (e.g., Parakrama Samudraya, Minneriya, Kantale, Kaudulla, Kalawewa and Giritale) [58]. After the construction of this reservoir was completed in the mid-1980s, the flood inundation risk in the downstream of the Mahaweli River was significantly reduced [58].…”

“…Kotmale reservoir was specifically designed for flood control by making it possible to transfer flood water from Polgolla to the dry zone where restored ancient tanks are located (e.g., Parakrama Samudraya, Minneriya, Kantale, Kaudulla, Kalawewa and Giritale) [58]. After the construction of this reservoir was completed in the mid-1980s, the flood inundation risk in the downstream of the Mahaweli River was significantly reduced [58]. The Mahaweli Authority also reestablished ancient river connections among Sudu Ganga, Amban Ganga, and Dambulla Oya in central province [59].…”

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“…Root Mean Squared Error (RMSE). RMSE is a widely used metric that measures the average magnitude of the errors between predicted (y i ) and observed (� y i ) values (Eq 2) [38]. It comprehensively assesses the model's accuracy, where lower values indicate better…”