Alireza Sepahvand; Nasrin Beiranvand; Negar Arjmand
Abstract
Water quality (WQ) is influenced by various variables, including natural ones like rainfall and erosion and human ones like urban, agricultural, and industry operations, that plays a very important role in assessment and determining factors such as environmental conditions, public health, economic and ...
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Water quality (WQ) is influenced by various variables, including natural ones like rainfall and erosion and human ones like urban, agricultural, and industry operations, that plays a very important role in assessment and determining factors such as environmental conditions, public health, economic and social progress and development. Therefore, temporal and spatial trending of water quality is necessary for planning water resource management. In this research, the performance of the six soft computing techniques, including, Random Forest, Reduced Error Pruning Tree (REPt), M5P model, bagging RF, bagging REPt and bagging M5P were compared to estimate the water quality index (WQI) in Khorramabad, Biranshahr and Alashtar sub-watersheds, Lorestan province, Iran. At first, based on water quality data, water quality index (WQI) was calculated and ten distinct water quality parameters (2014 to 2023) were used as input variables and WQI as output. Total data set consists of water quality parameters of three sub-watersheds out of which 70% data used to training and 30% data were used to testing phase. Finally, the models were compared with Correlation Coefficient (C.C.), Root Mean Square Error (RMSE), Maximum Absolute Error (MAE), Taylor diagram and Violin plot box. The obtained results suggest that the BM5P is more accurate to estimate the water quality index (WQI) compared to the M5P, ReepTree and Random Forest (RF) models for the given study area. According to the results of the test part of the BM5P model, it has given us the best result, which are the correlation coefficient, the Root Mean Square Error and the Mean Absolute Error 0.99, 0.2, and 0.15, respectively. Also, the Taylor diagram and violin box plot were concluded that BM5P was the most reliable soft computing technique for the prediction of WQI. Finally, the structure of Artificial Intelligence Techniques (AIT) for modeling is very simple and very less time consumable. Thus, the BM5P model can be useful in the water quality index (WQI) modeling not only for accuracy but also for its time-saving and simple structure compared with other models.
Nasrin Beiranvand; Alireza Sepahvand; Ali Haghizadeh
Abstract
In this study, five soft computing techniques, GP-PUK, GP-RBF, M5P, REEP Tree and RF were used to predict the SL in Cham Anjir, Bahram Joo, Kaka Reza and Sarab Syed Ali hydrometry stations in Khorramabad, Biranshahr and Alashtar sub-watersheds, Lorestan province. Total data set consists of rain, discharge ...
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In this study, five soft computing techniques, GP-PUK, GP-RBF, M5P, REEP Tree and RF were used to predict the SL in Cham Anjir, Bahram Joo, Kaka Reza and Sarab Syed Ali hydrometry stations in Khorramabad, Biranshahr and Alashtar sub-watersheds, Lorestan province. Total data set consists of rain, discharge and solute load (SL) of three sub-watersheds out of which 70% data used to training and 30% data were used to testing phase. Finally, the models’ accuracy was assessed using three performance evaluation parameters, which were Correlation Coefficient (C.C.), Root Mean Square Error (RMSE) and Maximum Absolute Error (MAE). Results suggest that GP-PUK and GP-RBF models works well than other modeling approaches in estimating the SL in low and high water-periods. The result showed that, In the high-water period, in Cham Anjir, Sarab Said Ali and Kaka Reza stations the GP-RBF model and in the Bahram Joo station the GP-PUK model with the highest C.C and the lowest error were selected the optimal models in estimating the SL. Also, in the low water period, result shown that in Cham Anjir, Sarab Said Ali and Bahram Joo stations the GP-RBF model and in the Kaka Reza station the GP-PUK model were the best models in estimating the SL. Therefore, these models can be used to estimate the solute load of nearby rivers by/without hydrometry station for the management of the quantity and quality of surface water.
