Development of wwm system using mlnn-ga for ph prediction of water quality in catchment area

Water quality is crucial to maintain as water is a necessity in our daily life. In aquatic ecosystem, good water quality allows aquatic life to have good health and improving their productivity leads to significant benefits to the environment. However, the method of collecting data is still in manual way. Data samples need to be collected and evaluated in the laboratory. It leads the results of water quality took a long time to retrieve and cannot be obtained continuously. Hence, a system is required to monitor the water quality that capable to obtain the results fast and continuously. In this research studies, Wireless Passive Water Quality Catchment Monitoring System or WMM System is introduced to collect the water quality parameters. Five parameters are measured in the system, which are potential hydrogen (pH), temperature, light intensity of water, coordinate of collecting data, and wave velocity. Primary lake at University Malaysia Pahang is selected as the experimental area for the WWM System. However, coverage area of the lake is large and require many WWM Systems to be developed. This method is expensive in terms of budget expenditure. Thus, prediction of water quality is applied to solve this problem with a number of WWM Systems used for collecting data will be reduced. Artificial Neural Network or ANN is one of the methods that commonly used to predict the quality of water. The structure and function of ANN is based on biological neural network. ANN process consists of three layer which are input layer, hidden layer, and output layer and weight of the network usually adjusted by using back-propagation (BP) technique. This technique has several advantages which are fast, simple, and easy to program. However, there are some weaknesses identified in BP such as being often trapped in local minima, sensitive to noisy data, and the performance of BP on certain problems is dependent on the input data. In this research study, Multi-Layer Neural Network optimized by Genetic Algorithm (MLNN-GA) is introduced to predict the water quality. This model will predict the pH value obtained by WWM System based on the value of pH surrounding within 100m2 area. MLNN-GA contain three hidden layers and Genetic Algorithm (GA) will optimize the weight of neural network during the training process. GA has several advantages, which are less often trapped in local minima and can operate in a complex process such as extended neural network. In training process of MLNN-GA, number of neurons in a hidden layer will be tested to obtain the best accuracy. The parameter of pH is chosen for predicting water quality as pH is one important parameter that can influence the condition of water. MLNN-GA is a new method proposed in this research study for predicting the water quality in terms of pH value. The results of prediction will be compared with BP technique and the average value of pH within the 100m2 area. The results of prediction obtained by MLNN-GA model with 99.64% accuracy has a significant potential to be used in the future and WWM System has a great potential to be used in monitoring water quality

Development of wireless passive water quality catchment monitoring system

To maintain the quality of aquatic ecosystems, good water quality is needed. The quality of water needs to be tracked in real-time for environmental protection and tracking pollution sources. This paper aims to describe the development and data acquired for water catchment quality monitoring by using a passive system which includes location tagging. Wireless Passive Water Quality Catchment Monitoring (WPWQCM) System is used to check and monitor water quality continuously. The condition of water in terms of acidity, temperature and light intensity needs to be monitored. WPWQCM System featured four sensors which are a temperature sensor, light intensity sensor, pH sensor and GPS tracker that will float in water to collect the data. GPS tracker on passive water catchment monitoring system is a new feature in the system where the location of water can be identified. With the extra feature, water quality can be mapped and in the future, the source of disturbance can be determined. UMP Lake was chosen to check and monitor the water quality. The system used wireless communication by using XBee Pro as a medium of communication between CT-Uno board and PC

Evaluating IoT based passive water catchment monitoring system data acquisition and analysis

Water quality is the main aspect to determine the quality of aquatic systems. Poor water quality will pose a health risk for people and ecosystems. The old methods such as collecting samples of water manually and testing and analysing at lab will cause the time consuming, wastage of man power and not economical. A system is needed to provide a real-time data for environmental protection and tracking pollution sources. This paper aims to describe on how to monitor water quality continuously through IoT platform. Water Quality Catchment Monitoring System was introduced to check and monitor water quality continuously. It’s features five sensors which are temperature sensor, light intensity sensor, pH sensor, GPS tracker and Inertia Movement Unit (IMU). IMU is a new feature in the system where the direction of x and y is determined for planning and find out where a water quality problem exists by determining the flow of water. The system uses an internet wireless connection using the ESP8266 Wi-Fi Shield Module as a connection between Arduino Mega2560 and laptop. ThingSpeak application acts as an IoT platform used for real-time data monitorin

Development of Wireless Passive Water Quality Catchment Monitoring System

To maintain the quality of aquatic ecosystems, good water quality is needed. The quality of water needs to be tracked in real-time for environmental protection and tracking pollution sources. This paper aims to describe the development and data acquired for water catchment quality monitoring by using a passive system which includes location tagging. Wireless Passive Water Quality Catchment Monitoring (WPWQCM) System is used to check and monitor water quality continuously. The condition of water in terms of acidity, temperature and light intensity needs to be monitored. WPWQCM System featured four sensors which are a temperature sensor, light intensity sensor, pH sensor and GPS tracker that will float in water to collect the data. GPS tracker on passive water catchment monitoring system is a new feature in the system where the location of water can be identified. With the extra feature, water quality can be mapped and in the future, the source of disturbance can be determined. UMP Lake was chosen to check and monitor the water quality. The system used wireless communication by using XBee Pro as a medium of communication between CT-Uno board and PC

Estimating the un-sampled ph value via neighbouring points using multi-layer neural network - genetic algorithm

This study shows a new method to estimate unsampled pH value by utilizing neighboring pH, which according to recent literature, has not been done yet. In investigating this method, three algorithms are used: Neural Network-Genetic Algorithm (MLNN-GA), MLNN with backpropagation (MLNN-BP), and averaging method. MLNNGA and MLNN-BP are inputted with four pH values from distant adjacent locations on a similar basin. MLNN-GA and MLNN-BP utilize GA and backpropagation respectively to update the weight. GA optimizer is used in MLNN-GA where the result of each learning weight will be the initial weight of the next learning process. All three methods are compared based on RMSE, MSE and MAPE. MLNN-GA yielded the lowest average RMSE =0.026265, average MSE =0.000886 and average MAPE =0.003985 compared to MLNN-BP (average RMSE =0.042644, average MSE =0.002648, average MAPE =0.006862) and averaging method (average RMSE =0.136629, average MSE = 0.026128, average MAPE =0.150400). Noticeably, estimating unsampled pH value utilizing neighboring pH by using MLNNGA shows a better performance than MLNN-BP and averaging method