Forecasting water quality parameters using artificial neural network for irrigation purposes.

This study was aimed at analyzing the water quality of Ele River Nnewi, Anambra State for irrigation purposes with a view to predicting a one-year water quality index using Artificial Neural Network (ANN). Water pollution has posed a major problem and identifying the points of pollution in the River system is a very difficult task. To overcome this task, the need to determine the pollution level arose by modeling and predicting four water quality parameters at four (4) different locations using the Artificial Neural Network. These parameters include the pH, Total Dissolved Solids (TDS), Electrical Conductivity (EC), and Sodium (Na), respectively. The water quality results showed that the pH values which ranges from 6.01 to 6.87 were within the FAO standard in all the points for both rainy and dry seasons, whereas the TDS (mg/l), EC (dS/m) and Na (mg/l) parametric values range from 2001 to 2506, 3.01 to 5.76, and 40.42 to 73.45 respectively, were above the FAO standard from point 1 to point 3 and falls within the FAO standard at point 4 with values ranging from 1003 to 1994, 2.01 to 2.78 and 31.24 to 39.44, respectively. However, during the dry season, the TDS, EC, and Na values range from 2002 to 2742, 3.04 to 5.82 and 40.14 to 88.45 respectively, were all above the FAO standard. Generally, the artificial neural network modeled the actual water quality data set very well with good prediction. The training model performance evaluation shows that the R2 values ranges from 0.981 to 0.990, 0.981 to 0.988, 0.981 to 0.989 and 0981 to 0.989, for pH, TDS, EC, and Na. The testing model performance shows that the R2 value ranges from 0.952 to 0.967, 0.953 to 0.970, 0.951 to 0.967and 0.953 to 0.968, for pH, TDS, EC and Na while the forecast performance evaluation shows that the R2 values ranges from 0.945 to 0.968, 0.946 to 0.968, 0.944 to 0.967 and 0.949 to 0.965 for pH, TDS, EC and Na respectively. It was also observed that the Root Mean Squared Error (RMSE) ranges from 0.022 to 0.088, 0.012 to 0.087, 0.015 to 0.085 and 0.014 to 0.084 for pH, TDS, EC and Na, respectively. Information from this study will serve as a guide to researchers on the water quality index for irrigation purposes. Also, it will guide the government and agencies on policy, management and decision-making on water resources.

A GIS based flood vulnerability modelling of Anambra State using an integrated IVFRN-DEMATEL-ANP model.

Flooding is a major environmental problem facing Anambra State of Nigeria, which also threatens food security in the state. To address this issue, continual flood vulnerability mapping exploring more efficient methods is needed to facilitate flood risk management in the state. The advantages of employing spatial information technologies such as Remote Sensing (RS) and Geographic Information System (GIS) in flood vulnerability mapping has been widely documented; the limitations of employing GIS alone in effective vulnerability analysis have also been documented by researchers. To overcome these limitations, this study adopted the use of GIS and the integration of Interval Value Fuzzy Rough Number (IVFRN), Decision Making Trial and Evaluation Laboratory (DEMATEL), and Analytical Network Process (ANP) method in vulnerability assessment of flood hazard. The result of the study shows that the state is very vulnerable to flood with 73% of the total area of the state lying between Very High and Medium vulnerable zones. The most vulnerable Local Government Area (LGA) in the State is Anambra West with 95% of the total area of the LGA lying between Very High and Medium vulnerable zones. Furthermore, the obtained values of R ˜ - D ˜ show that Rainfall Intensity factor is the major cause of flood in the study area with the highest positive value of 1.55 and Soil factor is the major effect with the highest negative value of -0.93. The IVFRN-DEMATEL-ANP assessment model was validated using AUC-ROC method; an AUC value of 0.946 was obtained, this indicates that the model has excellent prediction accuracy. This study was able to establish the feasibility of integrating the IVFRN, DEMATEL and ANP methods in flood vulnerability assessment. It is recommended that the provision of adequate drainage systems should be prioritized to areas of high flood vulnerability index; to help mitigate flood hazards in the State. Also, strategic planning of infrastructures and emergency routes for moving people and key assets from vulnerable areas especially during the rainy season should be geospatial-based and systematic.

Response Surface Methodology optimization of chito-protein synthesized from crab shell in treatment of abattoir wastewater.

Abattoir wastewater generated from various meat processing operations in several developing countries pose a serious threat to the environment. Consequently, there is urgent need to reduce the impact of environmental pollution from it. Coagulation techniques have been recommended and used by many researchers successfully in treating wastewater, therefore an investigation of possible use of chito-protein extracted from crab shell (locally sourced) was used as a coagulant for treating abattoir wastewater. Coagulation experiments were carried out using jar-test procedure to investigate the influence of pH, time of settling, temperature and adsorbent dosage for coagulation of BOD, COD, Turbidity and Colour from the wastewater sample. To determine the interaction effect of the various process variables, Response Surface Method (RSM) was used in the optimization of the process variables. To determine the effectiveness of the coagulant, pre and post characterization of the wastewater samples were undertaken, the result of the post characterization of the wastewater sample indicated that most of the water quality parameters except Iron were within WHO standard. The Total Suspended Solid (TSS), for instance stood at 564.6 mg/L and 29 mg/L respectively for pre and post characterisation, the value of 29 mg/L of the post characterization was below the WHO recommended value of 30 mg/L. The predicted responses and the experimental values correlated significantly, an indicator that RSM optimization method used in this study is suitable in modelling the process variables. The result of the study further shows that optimum process variable is dependent on the solution pH (acidic), coagulant dosage of 2-3g, settling time of 25-30 min and operating temperature from 323K to 333K. The coagulant used in this study, when compared with previous studies have shown to have strong potential for use as a coagulant and as an alternative to chemical coagulants in the treatment of abattoir wastewater.

Re-evaluation of the phylogenetic relationships and species delimitation of two closely related families (Lamiaceae and Verbenaceae) using two DNA barcode markers.

The families Lamiaceae and Verbenaceae comprise several closely related species that possess high morphological synapomorphic traits. Hence, there is a tendency of species misidentification using only the morphological characters. Herein, we evaluated the discriminatory power of the universal DNA barcodes (matK and rbcL) for 53 species spanning the two families. Using these markers, we inferred phylogenetic relationships and conducted species delimitation analysis using four delimitation methods: Automated Barcode Gap Discovery (ABGD), TaxonDNA, Bayesian Poisson Tree Processes (bPTP) and General Mixed Yule Coalescent (GMYC). The phylogenetic reconstruction based on the matK gene resolved the relationships between the families and further suggested the expansion of the Lamiaceae to include some core Verbanaceae genus, e.g., Gmelina. The rbcL marker using the TaxonDNA method displayed high species delimitation resolutions, while the ABGD, GMYC, and bPTP generated different number of Operational Taxonomic Units/genetic clusters. Our results underscored the efficiency of the matK and rbcL genes as reliable markers for resolving phylogenetic relationships and species delimitation of both families, respectively. The current study provides insights into the DNA barcode applications in these families, at the same time contributing to the current understanding of genetic divergence patterns in angiosperms.