Runoff Estimation Using Advanced Soft Computing Techniques: A Case Study of Mangla Watershed Pakistan

Abstract

A precise rainfall-runoff prediction is crucial for hydrology and the management of water resources. Rainfall-runoff prediction is a nonlinear method influenced by simulation model inputs. Previously employed methods have some limitations in predicting rainfall-runoff, such as low learning speed, overfitting issues, stopping criteria, and back-propagation issues. Therefore, this study uses distinctive soft computing approaches to overcome these issues for modeling rainfall-runoff for the Mangla watershed in Pakistan. Rainfall-runoff data for 29 years from 1978–2007 is used in the study to estimate runoff. The soft computing approaches used in the study are Tree Boost (TB), decision tree forests (DTFs), and single decision trees (SDTs). Using various combinations of past rainfall datasets, these soft computing techniques are validated and tested for the security of efficient results. The evaluation criteria for the models are some statistical measures consisting of root means square error (RMSE), mean absolute error (MAE), coefficient of determination (R2), and Nash–Sutcliffe efficiency (NSE). The outcomes of these computing techniques were evaluated with the multilayer perceptron (MLP). DTF was found to be a more accurate soft computing approach with the average evaluation parameters R2, NSE, RMSE, and MAE being 0.9, 0.8, 1000, and 7000 cumecs. Regarding R2 and RMSE, there are about 57% and 17% of improvement in the results of DTF compared to other techniques. Flow duration curves (FDCs) were employed and revealed that DTF performed better than other techniques. This assessment revealed that DTF has potential; researchers may consider it an alternative approach for rainfall-runoff estimations in the Mangla watershed.

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“…The authors used meteorological records like the temperature, humidity, and runoff datasets to forecast rainfall runoff. The results indicated that the decision tree could forecast the rainfall-runoff process more precisely than the other methods [36].…”

“…DTs have been used for rainfall, flood forecasting, and drought monitoring [24,36]. Figure 5 depicts the process of DTs, as described by Humphries et al (2022): data separating and error eradication proceed until the terminal node is reached or the dataset misclassification error at the termination of the terminal node becomes zero. At this point, further data splitting will discontinue.…”

“…At this point, further data splitting will discontinue. The output value is displayed on the terminal node generation's display [36].…”

“…The authors used meteorological records like the temperature, humidity, and runoff datasets to forecast rainfall runoff. The results indicated that the decision tree could forecast the rainfall-runoff process more precisely than the other methods[36].In another study,Ahmadi et al (2022) propose a new sequential minimal optimization (SMO) that develops ensembles for rainfall prediction utilizing random committee (RC), Dagging (DA), and additive regression (AR) models. In Kermanshah, Iran, a synoptic station was in operation between 1988 and 2018, and they collected thirty years' monthly datasets, including the highest and lowest relative humidity rates, temperatures, evaporation data, sunlight hours, wind speed, and rainfall.…”